02a_Eval_Oesophagus.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prospective Evaluation of Targeted Literature Search: Oesophageal Carcinoma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from dotenv import load_dotenv\n",
    "load_dotenv()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "OVERWRITE_LATEX_OUTPUT = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "GUIDELINE_ID = \"oesophaguskarzinom\"\n",
    "SCREENING_TF_MIN = \"09/01/2019\" # 09/01/2019 stated in report, but some PMIDs are included anyway\n",
    "SCREENING_TF_MAX = \"03/04/2022\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "PATH_PREFIX = \"esophagus\"\n",
    "PATH_GGPONC_LITERATURE = Path(\"data/screening/ggponc_v2.1_2023_03_30_literature.csv\")\n",
    "PATH_RIS_CONTENT = Path(f\"data/screening/{PATH_PREFIX}/ris_with_matched_ids.csv\")\n",
    "PATH_MANUAL_REVIEW = Path(f\"data/screening/{PATH_PREFIX}/manual_review.csv\")\n",
    "PATH_QUERY_RESULT = Path(f\"data/screening/{PATH_PREFIX}/retrieved_evidence.parquet\")\n",
    "PATH_UNSCREENED_HQ = Path(f\"data/screening/{PATH_PREFIX}/unscreened_hq.csv\")\n",
    "\n",
    "PATH_LATEX_TABLE_OUTPUT = Path(\n",
    "    f\"output/latex/from_python/tables/evaluation/{PATH_PREFIX}/\"\n",
    ")\n",
    "PATH_LATEX_PLOT_OUTPUT = Path(\n",
    "    f\"output/latex/from_python/figures/evaluation/{PATH_PREFIX}/\"\n",
    ")\n",
    "PATH_LATEX_TABLE_OUTPUT.mkdir(exist_ok=True, parents=True)\n",
    "PATH_LATEX_PLOT_OUTPUT.mkdir(exist_ok=True, parents=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from api.app import app\n",
    "from fastapi.testclient import TestClient\n",
    "import os\n",
    "\n",
    "with TestClient(app) as client:\n",
    "    # Initialize state\n",
    "    pass    \n",
    "\n",
    "session = app.state.session()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Screened evidence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_s = pd.read_csv(\n",
    "    PATH_RIS_CONTENT,\n",
    "    parse_dates=[\"publication_date\", \"article_date_api\"],\n",
    ").astype({\"pm_id\": \"Int64\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import ast\n",
    "\n",
    "df_s[\"publication_types\"] = df_s[\"publication_types\"].apply(ast.literal_eval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>is_rct_api</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>is_included</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>False</th>\n",
       "      <td>2643</td>\n",
       "      <td>302</td>\n",
       "      <td>2945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>52</td>\n",
       "      <td>29</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>2695</td>\n",
       "      <td>331</td>\n",
       "      <td>3026</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "is_rct_api   False  True   All\n",
       "is_included                   \n",
       "False         2643   302  2945\n",
       "True            52    29    81\n",
       "All           2695   331  3026"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df_s[\"is_included\"], df_s[\"is_rct_api\"], margins=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>is_rct_api</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>is_relevant</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>False</th>\n",
       "      <td>2640</td>\n",
       "      <td>296</td>\n",
       "      <td>2936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>55</td>\n",
       "      <td>35</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>2695</td>\n",
       "      <td>331</td>\n",
       "      <td>3026</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "is_rct_api   False  True   All\n",
       "is_relevant                   \n",
       "False         2640   296  2936\n",
       "True            55    35    90\n",
       "All           2695   331  3026"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df_s[\"is_relevant\"], df_s[\"is_rct_api\"], margins=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "screening_origin  is_relevant  is_rct_api\n",
       "manual            False        False            3\n",
       "                               True             6\n",
       "                  True         True             9\n",
       "original          False        False         2637\n",
       "                               True           290\n",
       "                  True         False           55\n",
       "                               True            26\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_s.groupby(['screening_origin', 'is_relevant', 'is_rct_api']).size()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Retrieved Evidence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "from integration.citation_utils import pm_id_to_publication_date\n",
    "from evaluation.matching import is_in_ggponc\n",
    "from tqdm.auto import tqdm\n",
    "import os\n",
    "from requests.exceptions import ConnectionError\n",
    "\n",
    "if PATH_QUERY_RESULT.exists():\n",
    "    df_query = pd.read_parquet(PATH_QUERY_RESULT).astype({\"pm_id\": \"Int64\"})\n",
    "else:\n",
    "    query = EvidenceQuery(guideline_id=GUIDELINE_ID)\n",
    "    try:\n",
    "        # fetch evidence from API\n",
    "        print(\"Fetching evidence from API\")\n",
    "        evidence = query_api_for_evidence(query=query)\n",
    "        df_query = evidence_to_df(evidence)\n",
    "        df_query[\"guideline_id\"] = GUIDELINE_ID\n",
    "        print(\"Done.\")\n",
    "\n",
    "        # retrieve exact article dates from Pubmed API\n",
    "        tqdm.pandas(desc=\"Fetching article dates\")\n",
    "        df_query[\"article_date_api\"] = pd.to_datetime(\n",
    "            df_query[\"pm_id\"].progress_apply(\n",
    "                lambda pm_id: pm_id_to_publication_date(\n",
    "                    pm_id=pm_id,\n",
    "                    email=os.environ.get(\"PUBMED_USER\"),\n",
    "                    api_key=os.environ.get(\"PUBMED_API_KEY\"),\n",
    "                    cache_path=\"data/literature/pm_id_to_entrez_xml.json\",\n",
    "                )\n",
    "            )\n",
    "        )\n",
    "\n",
    "        # exclude articles outside of screening timeframe\n",
    "        df_query[\"publication_date_combined\"] = df_query[\"article_date_api\"].fillna(\n",
    "            df_query[\"publication_date\"]\n",
    "        )\n",
    "        df_query = df_query.query(\n",
    "            \"(publication_date_combined >= @SCREENING_TF_MIN & publication_date_combined <= @SCREENING_TF_MAX)\"\n",
    "        )\n",
    "\n",
    "        # check presence in GGPONC\n",
    "        df_ggponc = (\n",
    "            pd.read_csv(PATH_GGPONC_LITERATURE)\n",
    "            .astype({\"pm_id\": \"Int64\"})\n",
    "            .query(\"guideline_id == @GUIDELINE_ID\")\n",
    "        )\n",
    "        ggponc_pm_ids = set(df_ggponc[\"pm_id\"].dropna())\n",
    "        ggponc_titles = set(df_ggponc[\"title\"])\n",
    "\n",
    "        tqdm.pandas(desc=\"Checking for presence in GGPONC\")\n",
    "        df_query = df_query.progress_apply(  # type: ignore\n",
    "            lambda row: is_in_ggponc(\n",
    "                row=row, ggponc_pm_ids=ggponc_pm_ids, ggponc_titles=ggponc_titles\n",
    "            ),\n",
    "            axis=1,\n",
    "        )\n",
    "\n",
    "        df_query.to_parquet(PATH_QUERY_RESULT)\n",
    "    except ConnectionError:\n",
    "        print(\"Can't connect to the API, is it running?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>is_in_ggponc</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>source</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Civic</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ClinicalTrials</th>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pubmed</th>\n",
       "      <td>316</td>\n",
       "      <td>10</td>\n",
       "      <td>326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>360</td>\n",
       "      <td>10</td>\n",
       "      <td>370</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "is_in_ggponc    False  True  All\n",
       "source                          \n",
       "Civic               4     0    4\n",
       "ClinicalTrials     40     0   40\n",
       "Pubmed            316    10  326\n",
       "All               360    10  370"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df_query[\"source\"], df_query[\"is_in_ggponc\"], margins=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>is_in_ggponc</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>is_included</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>False</th>\n",
       "      <td>2934</td>\n",
       "      <td>11</td>\n",
       "      <td>2945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>76</td>\n",
       "      <td>5</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>3010</td>\n",
       "      <td>16</td>\n",
       "      <td>3026</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "is_in_ggponc  False  True   All\n",
       "is_included                    \n",
       "False          2934    11  2945\n",
       "True             76     5    81\n",
       "All            3010    16  3026"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df_s[\"is_included\"], df_s[\"is_in_ggponc\"], margins=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>is_in_ggponc</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>is_relevant</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>False</th>\n",
       "      <td>2934</td>\n",
       "      <td>2</td>\n",
       "      <td>2936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>76</td>\n",
       "      <td>14</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>3010</td>\n",
       "      <td>16</td>\n",
       "      <td>3026</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "is_in_ggponc  False  True   All\n",
       "is_relevant                    \n",
       "False          2934     2  2936\n",
       "True             76    14    90\n",
       "All            3010    16  3026"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df_s[\"is_relevant\"], df_s[\"is_in_ggponc\"], margins=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "screening_origin  is_relevant  is_rct_api\n",
       "manual            False        False            3\n",
       "                               True             6\n",
       "                  True         True             9\n",
       "original          False        False         2637\n",
       "                               True           290\n",
       "                  True         False           55\n",
       "                               True            26\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_s.groupby(['screening_origin', 'is_relevant', 'is_rct_api']).size()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Retrieval Evaluation using the Pubmed data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the evaluation, we only look at results retrieved from Pubmed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_r = df_query.query(\"source == 'Pubmed'\").copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check if CIViC results are already in PubMed result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "34912120 True\n",
      "32469182 True\n",
      "32308431 False\n",
      "31908417 False\n"
     ]
    }
   ],
   "source": [
    "df_civic = df_query.query(\"source == 'Civic'\")\n",
    "for pm_id in df_civic.pm_id:\n",
    "    print(pm_id, pm_id in df_r.pm_id.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Furthermore, we consider all results that we can retrieve from GGPONC as having been screened and included, thus making them relevant. We therefor add these records to the dataframe containing the screened records:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from evaluation.matching import add_unscreened_ggponc_citations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_s_with_ggponc = add_unscreened_ggponc_citations(df_s, df_r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from evaluation.metrics import FILTERS_SCREENED, FILTERS_RETRIEVED"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluating screened document subset 'All'\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7ef43c9363f94a1e839443591b81128d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Evaluating filter combinations:   0%|          | 0/14 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluating screened document subset 'Only RCTs'\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ccd3fd6b03514b43816181c3edaf1a6d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Evaluating filter combinations:   0%|          | 0/14 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from evaluation.metrics import run_evaluation\n",
    "\n",
    "metrics = run_evaluation(\n",
    "    df_s_with_ggponc.query(\"screening_origin == 'original'\"),\n",
    "    df_r,\n",
    "    filters_screened=FILTERS_SCREENED,\n",
    "    filters_retrieved=FILTERS_RETRIEVED,\n",
    "    col_id=\"pm_id\",\n",
    "    col_relevant=\"is_relevant\",\n",
    "    compute_combinations=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from evaluation.plotting import draw_venn_rsi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### All evidence with a Pubmed ID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 551.181x340.649 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_venn_rsi(\n",
    "    title=\"All\",\n",
    "    ids_retrieved=metrics[\"All\"][\"All\"][\"ids_retrieved\"],\n",
    "    ids_screened=metrics[\"All\"][\"All\"][\"ids_screened\"],\n",
    "    ids_screened_relevant=metrics[\"All\"][\"All\"][\"ids_screened_relevant\"],\n",
    "    add_pn_labels=True,\n",
    ");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from evaluation.metrics import metrics_to_df, highlight_metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_7f262_row0_col1, #T_7f262_row0_col3, #T_7f262_row0_col5, #T_7f262_row2_col1, #T_7f262_row2_col3, #T_7f262_row2_col5, #T_7f262_row3_col1, #T_7f262_row3_col3, #T_7f262_row3_col5, #T_7f262_row5_col0, #T_7f262_row5_col2, #T_7f262_row5_col4, #T_7f262_row9_col1, #T_7f262_row9_col3, #T_7f262_row9_col5 {\n",
       "  font-weight: bold;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_7f262\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_7f262_level0_col0\" class=\"col_heading level0 col0\" >Precision</th>\n",
       "      <th id=\"T_7f262_level0_col1\" class=\"col_heading level0 col1\" >Recall</th>\n",
       "      <th id=\"T_7f262_level0_col2\" class=\"col_heading level0 col2\" >$F_1$</th>\n",
       "      <th id=\"T_7f262_level0_col3\" class=\"col_heading level0 col3\" >TP</th>\n",
       "      <th id=\"T_7f262_level0_col4\" class=\"col_heading level0 col4\" >FP</th>\n",
       "      <th id=\"T_7f262_level0_col5\" class=\"col_heading level0 col5\" >FN</th>\n",
       "      <th id=\"T_7f262_level0_col6\" class=\"col_heading level0 col6\" >TN</th>\n",
       "      <th id=\"T_7f262_level0_col7\" class=\"col_heading level0 col7\" >?</th>\n",
       "      <th id=\"T_7f262_level0_col8\" class=\"col_heading level0 col8\" >Retrieved</th>\n",
       "      <th id=\"T_7f262_level0_col9\" class=\"col_heading level0 col9\" >Screened</th>\n",
       "      <th id=\"T_7f262_level0_col10\" class=\"col_heading level0 col10\" >Relevant</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_7f262_level0_row0\" class=\"row_heading level0 row0\" >All</th>\n",
       "      <td id=\"T_7f262_row0_col0\" class=\"data row0 col0\" >0.1030</td>\n",
       "      <td id=\"T_7f262_row0_col1\" class=\"data row0 col1\" >0.2963</td>\n",
       "      <td id=\"T_7f262_row0_col2\" class=\"data row0 col2\" >0.152900</td>\n",
       "      <td id=\"T_7f262_row0_col3\" class=\"data row0 col3\" >24</td>\n",
       "      <td id=\"T_7f262_row0_col4\" class=\"data row0 col4\" >209</td>\n",
       "      <td id=\"T_7f262_row0_col5\" class=\"data row0 col5\" >57</td>\n",
       "      <td id=\"T_7f262_row0_col6\" class=\"data row0 col6\" >1873</td>\n",
       "      <td id=\"T_7f262_row0_col7\" class=\"data row0 col7\" >93</td>\n",
       "      <td id=\"T_7f262_row0_col8\" class=\"data row0 col8\" >326</td>\n",
       "      <td id=\"T_7f262_row0_col9\" class=\"data row0 col9\" >2163</td>\n",
       "      <td id=\"T_7f262_row0_col10\" class=\"data row0 col10\" >81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7f262_level0_row1\" class=\"row_heading level0 row1\" >Is RCT</th>\n",
       "      <td id=\"T_7f262_row1_col0\" class=\"data row1 col0\" >0.1358</td>\n",
       "      <td id=\"T_7f262_row1_col1\" class=\"data row1 col1\" >0.2716</td>\n",
       "      <td id=\"T_7f262_row1_col2\" class=\"data row1 col2\" >0.181100</td>\n",
       "      <td id=\"T_7f262_row1_col3\" class=\"data row1 col3\" >22</td>\n",
       "      <td id=\"T_7f262_row1_col4\" class=\"data row1 col4\" >140</td>\n",
       "      <td id=\"T_7f262_row1_col5\" class=\"data row1 col5\" >59</td>\n",
       "      <td id=\"T_7f262_row1_col6\" class=\"data row1 col6\" >1942</td>\n",
       "      <td id=\"T_7f262_row1_col7\" class=\"data row1 col7\" >48</td>\n",
       "      <td id=\"T_7f262_row1_col8\" class=\"data row1 col8\" >210</td>\n",
       "      <td id=\"T_7f262_row1_col9\" class=\"data row1 col9\" >2163</td>\n",
       "      <td id=\"T_7f262_row1_col10\" class=\"data row1 col10\" >81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7f262_level0_row2\" class=\"row_heading level0 row2\" >Non-empty Abstract</th>\n",
       "      <td id=\"T_7f262_row2_col0\" class=\"data row2 col0\" >0.1030</td>\n",
       "      <td id=\"T_7f262_row2_col1\" class=\"data row2 col1\" >0.2963</td>\n",
       "      <td id=\"T_7f262_row2_col2\" class=\"data row2 col2\" >0.152900</td>\n",
       "      <td id=\"T_7f262_row2_col3\" class=\"data row2 col3\" >24</td>\n",
       "      <td id=\"T_7f262_row2_col4\" class=\"data row2 col4\" >209</td>\n",
       "      <td id=\"T_7f262_row2_col5\" class=\"data row2 col5\" >57</td>\n",
       "      <td id=\"T_7f262_row2_col6\" class=\"data row2 col6\" >1873</td>\n",
       "      <td id=\"T_7f262_row2_col7\" class=\"data row2 col7\" >89</td>\n",
       "      <td id=\"T_7f262_row2_col8\" class=\"data row2 col8\" >322</td>\n",
       "      <td id=\"T_7f262_row2_col9\" class=\"data row2 col9\" >2163</td>\n",
       "      <td id=\"T_7f262_row2_col10\" class=\"data row2 col10\" >81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7f262_level0_row3\" class=\"row_heading level0 row3\" >Non-pediatric population</th>\n",
       "      <td id=\"T_7f262_row3_col0\" class=\"data row3 col0\" >0.1034</td>\n",
       "      <td id=\"T_7f262_row3_col1\" class=\"data row3 col1\" >0.2963</td>\n",
       "      <td id=\"T_7f262_row3_col2\" class=\"data row3 col2\" >0.153400</td>\n",
       "      <td id=\"T_7f262_row3_col3\" class=\"data row3 col3\" >24</td>\n",
       "      <td id=\"T_7f262_row3_col4\" class=\"data row3 col4\" >208</td>\n",
       "      <td id=\"T_7f262_row3_col5\" class=\"data row3 col5\" >57</td>\n",
       "      <td id=\"T_7f262_row3_col6\" class=\"data row3 col6\" >1874</td>\n",
       "      <td id=\"T_7f262_row3_col7\" class=\"data row3 col7\" >93</td>\n",
       "      <td id=\"T_7f262_row3_col8\" class=\"data row3 col8\" >325</td>\n",
       "      <td id=\"T_7f262_row3_col9\" class=\"data row3 col9\" >2163</td>\n",
       "      <td id=\"T_7f262_row3_col10\" class=\"data row3 col10\" >81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7f262_level0_row4\" class=\"row_heading level0 row4\" >Phase >= 2</th>\n",
       "      <td id=\"T_7f262_row4_col0\" class=\"data row4 col0\" >0.1587</td>\n",
       "      <td id=\"T_7f262_row4_col1\" class=\"data row4 col1\" >0.2469</td>\n",
       "      <td id=\"T_7f262_row4_col2\" class=\"data row4 col2\" >0.193200</td>\n",
       "      <td id=\"T_7f262_row4_col3\" class=\"data row4 col3\" >20</td>\n",
       "      <td id=\"T_7f262_row4_col4\" class=\"data row4 col4\" >106</td>\n",
       "      <td id=\"T_7f262_row4_col5\" class=\"data row4 col5\" >61</td>\n",
       "      <td id=\"T_7f262_row4_col6\" class=\"data row4 col6\" >1976</td>\n",
       "      <td id=\"T_7f262_row4_col7\" class=\"data row4 col7\" >36</td>\n",
       "      <td id=\"T_7f262_row4_col8\" class=\"data row4 col8\" >162</td>\n",
       "      <td id=\"T_7f262_row4_col9\" class=\"data row4 col9\" >2163</td>\n",
       "      <td id=\"T_7f262_row4_col10\" class=\"data row4 col10\" >81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7f262_level0_row5\" class=\"row_heading level0 row5\" >Phase >= 3</th>\n",
       "      <td id=\"T_7f262_row5_col0\" class=\"data row5 col0\" >0.3091</td>\n",
       "      <td id=\"T_7f262_row5_col1\" class=\"data row5 col1\" >0.2099</td>\n",
       "      <td id=\"T_7f262_row5_col2\" class=\"data row5 col2\" >0.250000</td>\n",
       "      <td id=\"T_7f262_row5_col3\" class=\"data row5 col3\" >17</td>\n",
       "      <td id=\"T_7f262_row5_col4\" class=\"data row5 col4\" >38</td>\n",
       "      <td id=\"T_7f262_row5_col5\" class=\"data row5 col5\" >64</td>\n",
       "      <td id=\"T_7f262_row5_col6\" class=\"data row5 col6\" >2044</td>\n",
       "      <td id=\"T_7f262_row5_col7\" class=\"data row5 col7\" >8</td>\n",
       "      <td id=\"T_7f262_row5_col8\" class=\"data row5 col8\" >63</td>\n",
       "      <td id=\"T_7f262_row5_col9\" class=\"data row5 col9\" >2163</td>\n",
       "      <td id=\"T_7f262_row5_col10\" class=\"data row5 col10\" >81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7f262_level0_row6\" class=\"row_heading level0 row6\" >Sample Size >= 5</th>\n",
       "      <td id=\"T_7f262_row6_col0\" class=\"data row6 col0\" >0.1031</td>\n",
       "      <td id=\"T_7f262_row6_col1\" class=\"data row6 col1\" >0.2840</td>\n",
       "      <td id=\"T_7f262_row6_col2\" class=\"data row6 col2\" >0.151300</td>\n",
       "      <td id=\"T_7f262_row6_col3\" class=\"data row6 col3\" >23</td>\n",
       "      <td id=\"T_7f262_row6_col4\" class=\"data row6 col4\" >200</td>\n",
       "      <td id=\"T_7f262_row6_col5\" class=\"data row6 col5\" >58</td>\n",
       "      <td id=\"T_7f262_row6_col6\" class=\"data row6 col6\" >1882</td>\n",
       "      <td id=\"T_7f262_row6_col7\" class=\"data row6 col7\" >87</td>\n",
       "      <td id=\"T_7f262_row6_col8\" class=\"data row6 col8\" >310</td>\n",
       "      <td id=\"T_7f262_row6_col9\" class=\"data row6 col9\" >2163</td>\n",
       "      <td id=\"T_7f262_row6_col10\" class=\"data row6 col10\" >81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7f262_level0_row7\" class=\"row_heading level0 row7\" >Sample Size >= 10</th>\n",
       "      <td id=\"T_7f262_row7_col0\" class=\"data row7 col0\" >0.1045</td>\n",
       "      <td id=\"T_7f262_row7_col1\" class=\"data row7 col1\" >0.2840</td>\n",
       "      <td id=\"T_7f262_row7_col2\" class=\"data row7 col2\" >0.152800</td>\n",
       "      <td id=\"T_7f262_row7_col3\" class=\"data row7 col3\" >23</td>\n",
       "      <td id=\"T_7f262_row7_col4\" class=\"data row7 col4\" >197</td>\n",
       "      <td id=\"T_7f262_row7_col5\" class=\"data row7 col5\" >58</td>\n",
       "      <td id=\"T_7f262_row7_col6\" class=\"data row7 col6\" >1885</td>\n",
       "      <td id=\"T_7f262_row7_col7\" class=\"data row7 col7\" >86</td>\n",
       "      <td id=\"T_7f262_row7_col8\" class=\"data row7 col8\" >306</td>\n",
       "      <td id=\"T_7f262_row7_col9\" class=\"data row7 col9\" >2163</td>\n",
       "      <td id=\"T_7f262_row7_col10\" class=\"data row7 col10\" >81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7f262_level0_row8\" class=\"row_heading level0 row8\" >Sample Size >= 15</th>\n",
       "      <td id=\"T_7f262_row8_col0\" class=\"data row8 col0\" >0.1065</td>\n",
       "      <td id=\"T_7f262_row8_col1\" class=\"data row8 col1\" >0.2840</td>\n",
       "      <td id=\"T_7f262_row8_col2\" class=\"data row8 col2\" >0.154900</td>\n",
       "      <td id=\"T_7f262_row8_col3\" class=\"data row8 col3\" >23</td>\n",
       "      <td id=\"T_7f262_row8_col4\" class=\"data row8 col4\" >193</td>\n",
       "      <td id=\"T_7f262_row8_col5\" class=\"data row8 col5\" >58</td>\n",
       "      <td id=\"T_7f262_row8_col6\" class=\"data row8 col6\" >1889</td>\n",
       "      <td id=\"T_7f262_row8_col7\" class=\"data row8 col7\" >80</td>\n",
       "      <td id=\"T_7f262_row8_col8\" class=\"data row8 col8\" >296</td>\n",
       "      <td id=\"T_7f262_row8_col9\" class=\"data row8 col9\" >2163</td>\n",
       "      <td id=\"T_7f262_row8_col10\" class=\"data row8 col10\" >81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7f262_level0_row9\" class=\"row_heading level0 row9\" >Known Intervention</th>\n",
       "      <td id=\"T_7f262_row9_col0\" class=\"data row9 col0\" >0.1212</td>\n",
       "      <td id=\"T_7f262_row9_col1\" class=\"data row9 col1\" >0.2963</td>\n",
       "      <td id=\"T_7f262_row9_col2\" class=\"data row9 col2\" >0.172000</td>\n",
       "      <td id=\"T_7f262_row9_col3\" class=\"data row9 col3\" >24</td>\n",
       "      <td id=\"T_7f262_row9_col4\" class=\"data row9 col4\" >174</td>\n",
       "      <td id=\"T_7f262_row9_col5\" class=\"data row9 col5\" >57</td>\n",
       "      <td id=\"T_7f262_row9_col6\" class=\"data row9 col6\" >1908</td>\n",
       "      <td id=\"T_7f262_row9_col7\" class=\"data row9 col7\" >65</td>\n",
       "      <td id=\"T_7f262_row9_col8\" class=\"data row9 col8\" >263</td>\n",
       "      <td id=\"T_7f262_row9_col9\" class=\"data row9 col9\" >2163</td>\n",
       "      <td id=\"T_7f262_row9_col10\" class=\"data row9 col10\" >81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7f262_level0_row10\" class=\"row_heading level0 row10\" >Unknown Intervention</th>\n",
       "      <td id=\"T_7f262_row10_col0\" class=\"data row10 col0\" >0.0752</td>\n",
       "      <td id=\"T_7f262_row10_col1\" class=\"data row10 col1\" >0.1235</td>\n",
       "      <td id=\"T_7f262_row10_col2\" class=\"data row10 col2\" >0.093500</td>\n",
       "      <td id=\"T_7f262_row10_col3\" class=\"data row10 col3\" >10</td>\n",
       "      <td id=\"T_7f262_row10_col4\" class=\"data row10 col4\" >123</td>\n",
       "      <td id=\"T_7f262_row10_col5\" class=\"data row10 col5\" >71</td>\n",
       "      <td id=\"T_7f262_row10_col6\" class=\"data row10 col6\" >1959</td>\n",
       "      <td id=\"T_7f262_row10_col7\" class=\"data row10 col7\" >56</td>\n",
       "      <td id=\"T_7f262_row10_col8\" class=\"data row10 col8\" >189</td>\n",
       "      <td id=\"T_7f262_row10_col9\" class=\"data row10 col9\" >2163</td>\n",
       "      <td id=\"T_7f262_row10_col10\" class=\"data row10 col10\" >81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7f262_level0_row11\" class=\"row_heading level0 row11\" >K+U Intervention</th>\n",
       "      <td id=\"T_7f262_row11_col0\" class=\"data row11 col0\" >0.0885</td>\n",
       "      <td id=\"T_7f262_row11_col1\" class=\"data row11 col1\" >0.1235</td>\n",
       "      <td id=\"T_7f262_row11_col2\" class=\"data row11 col2\" >0.103100</td>\n",
       "      <td id=\"T_7f262_row11_col3\" class=\"data row11 col3\" >10</td>\n",
       "      <td id=\"T_7f262_row11_col4\" class=\"data row11 col4\" >103</td>\n",
       "      <td id=\"T_7f262_row11_col5\" class=\"data row11 col5\" >71</td>\n",
       "      <td id=\"T_7f262_row11_col6\" class=\"data row11 col6\" >1979</td>\n",
       "      <td id=\"T_7f262_row11_col7\" class=\"data row11 col7\" >42</td>\n",
       "      <td id=\"T_7f262_row11_col8\" class=\"data row11 col8\" >155</td>\n",
       "      <td id=\"T_7f262_row11_col9\" class=\"data row11 col9\" >2163</td>\n",
       "      <td id=\"T_7f262_row11_col10\" class=\"data row11 col10\" >81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7f262_level0_row12\" class=\"row_heading level0 row12\" >Significant Finding</th>\n",
       "      <td id=\"T_7f262_row12_col0\" class=\"data row12 col0\" >0.1013</td>\n",
       "      <td id=\"T_7f262_row12_col1\" class=\"data row12 col1\" >0.1975</td>\n",
       "      <td id=\"T_7f262_row12_col2\" class=\"data row12 col2\" >0.133900</td>\n",
       "      <td id=\"T_7f262_row12_col3\" class=\"data row12 col3\" >16</td>\n",
       "      <td id=\"T_7f262_row12_col4\" class=\"data row12 col4\" >142</td>\n",
       "      <td id=\"T_7f262_row12_col5\" class=\"data row12 col5\" >65</td>\n",
       "      <td id=\"T_7f262_row12_col6\" class=\"data row12 col6\" >1940</td>\n",
       "      <td id=\"T_7f262_row12_col7\" class=\"data row12 col7\" >65</td>\n",
       "      <td id=\"T_7f262_row12_col8\" class=\"data row12 col8\" >223</td>\n",
       "      <td id=\"T_7f262_row12_col9\" class=\"data row12 col9\" >2163</td>\n",
       "      <td id=\"T_7f262_row12_col10\" class=\"data row12 col10\" >81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7f262_level0_row13\" class=\"row_heading level0 row13\" >No Significant Finding</th>\n",
       "      <td id=\"T_7f262_row13_col0\" class=\"data row13 col0\" >0.1067</td>\n",
       "      <td id=\"T_7f262_row13_col1\" class=\"data row13 col1\" >0.0988</td>\n",
       "      <td id=\"T_7f262_row13_col2\" class=\"data row13 col2\" >0.102600</td>\n",
       "      <td id=\"T_7f262_row13_col3\" class=\"data row13 col3\" >8</td>\n",
       "      <td id=\"T_7f262_row13_col4\" class=\"data row13 col4\" >67</td>\n",
       "      <td id=\"T_7f262_row13_col5\" class=\"data row13 col5\" >73</td>\n",
       "      <td id=\"T_7f262_row13_col6\" class=\"data row13 col6\" >2015</td>\n",
       "      <td id=\"T_7f262_row13_col7\" class=\"data row13 col7\" >24</td>\n",
       "      <td id=\"T_7f262_row13_col8\" class=\"data row13 col8\" >99</td>\n",
       "      <td id=\"T_7f262_row13_col9\" class=\"data row13 col9\" >2163</td>\n",
       "      <td id=\"T_7f262_row13_col10\" class=\"data row13 col10\" >81</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f68bc81fb50>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "highlight_metrics(\n",
    "    metrics_to_df(metrics[\"All\"], drop_zero_tp=False).head(len(FILTERS_RETRIEVED))\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### RCTs only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 551.181x340.649 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_venn_rsi(\n",
    "    title=\"RCTs only\",\n",
    "    ids_retrieved=metrics[\"Only RCTs\"][\"Is RCT\"][\"ids_retrieved\"],\n",
    "    ids_screened=metrics[\"Only RCTs\"][\"Is RCT\"][\"ids_screened\"],\n",
    "    ids_screened_relevant=metrics[\"Only RCTs\"][\"Is RCT\"][\"ids_screened_relevant\"],\n",
    "    add_pn_labels=True,\n",
    ");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_e8333_row0_col1, #T_e8333_row0_col3, #T_e8333_row0_col5, #T_e8333_row1_col1, #T_e8333_row1_col3, #T_e8333_row1_col5, #T_e8333_row2_col1, #T_e8333_row2_col3, #T_e8333_row2_col5, #T_e8333_row3_col1, #T_e8333_row3_col3, #T_e8333_row3_col5, #T_e8333_row5_col0, #T_e8333_row5_col2, #T_e8333_row5_col4, #T_e8333_row9_col1, #T_e8333_row9_col3, #T_e8333_row9_col5 {\n",
       "  font-weight: bold;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_e8333\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_e8333_level0_col0\" class=\"col_heading level0 col0\" >Precision</th>\n",
       "      <th id=\"T_e8333_level0_col1\" class=\"col_heading level0 col1\" >Recall</th>\n",
       "      <th id=\"T_e8333_level0_col2\" class=\"col_heading level0 col2\" >$F_1$</th>\n",
       "      <th id=\"T_e8333_level0_col3\" class=\"col_heading level0 col3\" >TP</th>\n",
       "      <th id=\"T_e8333_level0_col4\" class=\"col_heading level0 col4\" >FP</th>\n",
       "      <th id=\"T_e8333_level0_col5\" class=\"col_heading level0 col5\" >FN</th>\n",
       "      <th id=\"T_e8333_level0_col6\" class=\"col_heading level0 col6\" >TN</th>\n",
       "      <th id=\"T_e8333_level0_col7\" class=\"col_heading level0 col7\" >?</th>\n",
       "      <th id=\"T_e8333_level0_col8\" class=\"col_heading level0 col8\" >Retrieved</th>\n",
       "      <th id=\"T_e8333_level0_col9\" class=\"col_heading level0 col9\" >Screened</th>\n",
       "      <th id=\"T_e8333_level0_col10\" class=\"col_heading level0 col10\" >Relevant</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_e8333_level0_row0\" class=\"row_heading level0 row0\" >All</th>\n",
       "      <td id=\"T_e8333_row0_col0\" class=\"data row0 col0\" >0.1358</td>\n",
       "      <td id=\"T_e8333_row0_col1\" class=\"data row0 col1\" >0.8462</td>\n",
       "      <td id=\"T_e8333_row0_col2\" class=\"data row0 col2\" >0.234000</td>\n",
       "      <td id=\"T_e8333_row0_col3\" class=\"data row0 col3\" >22</td>\n",
       "      <td id=\"T_e8333_row0_col4\" class=\"data row0 col4\" >140</td>\n",
       "      <td id=\"T_e8333_row0_col5\" class=\"data row0 col5\" >4</td>\n",
       "      <td id=\"T_e8333_row0_col6\" class=\"data row0 col6\" >150</td>\n",
       "      <td id=\"T_e8333_row0_col7\" class=\"data row0 col7\" >164</td>\n",
       "      <td id=\"T_e8333_row0_col8\" class=\"data row0 col8\" >326</td>\n",
       "      <td id=\"T_e8333_row0_col9\" class=\"data row0 col9\" >316</td>\n",
       "      <td id=\"T_e8333_row0_col10\" class=\"data row0 col10\" >26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e8333_level0_row1\" class=\"row_heading level0 row1\" >Is RCT</th>\n",
       "      <td id=\"T_e8333_row1_col0\" class=\"data row1 col0\" >0.1358</td>\n",
       "      <td id=\"T_e8333_row1_col1\" class=\"data row1 col1\" >0.8462</td>\n",
       "      <td id=\"T_e8333_row1_col2\" class=\"data row1 col2\" >0.234000</td>\n",
       "      <td id=\"T_e8333_row1_col3\" class=\"data row1 col3\" >22</td>\n",
       "      <td id=\"T_e8333_row1_col4\" class=\"data row1 col4\" >140</td>\n",
       "      <td id=\"T_e8333_row1_col5\" class=\"data row1 col5\" >4</td>\n",
       "      <td id=\"T_e8333_row1_col6\" class=\"data row1 col6\" >150</td>\n",
       "      <td id=\"T_e8333_row1_col7\" class=\"data row1 col7\" >48</td>\n",
       "      <td id=\"T_e8333_row1_col8\" class=\"data row1 col8\" >210</td>\n",
       "      <td id=\"T_e8333_row1_col9\" class=\"data row1 col9\" >316</td>\n",
       "      <td id=\"T_e8333_row1_col10\" class=\"data row1 col10\" >26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e8333_level0_row2\" class=\"row_heading level0 row2\" >Non-empty Abstract</th>\n",
       "      <td id=\"T_e8333_row2_col0\" class=\"data row2 col0\" >0.1358</td>\n",
       "      <td id=\"T_e8333_row2_col1\" class=\"data row2 col1\" >0.8462</td>\n",
       "      <td id=\"T_e8333_row2_col2\" class=\"data row2 col2\" >0.234000</td>\n",
       "      <td id=\"T_e8333_row2_col3\" class=\"data row2 col3\" >22</td>\n",
       "      <td id=\"T_e8333_row2_col4\" class=\"data row2 col4\" >140</td>\n",
       "      <td id=\"T_e8333_row2_col5\" class=\"data row2 col5\" >4</td>\n",
       "      <td id=\"T_e8333_row2_col6\" class=\"data row2 col6\" >150</td>\n",
       "      <td id=\"T_e8333_row2_col7\" class=\"data row2 col7\" >160</td>\n",
       "      <td id=\"T_e8333_row2_col8\" class=\"data row2 col8\" >322</td>\n",
       "      <td id=\"T_e8333_row2_col9\" class=\"data row2 col9\" >316</td>\n",
       "      <td id=\"T_e8333_row2_col10\" class=\"data row2 col10\" >26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e8333_level0_row3\" class=\"row_heading level0 row3\" >Non-pediatric population</th>\n",
       "      <td id=\"T_e8333_row3_col0\" class=\"data row3 col0\" >0.1366</td>\n",
       "      <td id=\"T_e8333_row3_col1\" class=\"data row3 col1\" >0.8462</td>\n",
       "      <td id=\"T_e8333_row3_col2\" class=\"data row3 col2\" >0.235300</td>\n",
       "      <td id=\"T_e8333_row3_col3\" class=\"data row3 col3\" >22</td>\n",
       "      <td id=\"T_e8333_row3_col4\" class=\"data row3 col4\" >139</td>\n",
       "      <td id=\"T_e8333_row3_col5\" class=\"data row3 col5\" >4</td>\n",
       "      <td id=\"T_e8333_row3_col6\" class=\"data row3 col6\" >151</td>\n",
       "      <td id=\"T_e8333_row3_col7\" class=\"data row3 col7\" >164</td>\n",
       "      <td id=\"T_e8333_row3_col8\" class=\"data row3 col8\" >325</td>\n",
       "      <td id=\"T_e8333_row3_col9\" class=\"data row3 col9\" >316</td>\n",
       "      <td id=\"T_e8333_row3_col10\" class=\"data row3 col10\" >26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e8333_level0_row4\" class=\"row_heading level0 row4\" >Phase >= 2</th>\n",
       "      <td id=\"T_e8333_row4_col0\" class=\"data row4 col0\" >0.2278</td>\n",
       "      <td id=\"T_e8333_row4_col1\" class=\"data row4 col1\" >0.6923</td>\n",
       "      <td id=\"T_e8333_row4_col2\" class=\"data row4 col2\" >0.342900</td>\n",
       "      <td id=\"T_e8333_row4_col3\" class=\"data row4 col3\" >18</td>\n",
       "      <td id=\"T_e8333_row4_col4\" class=\"data row4 col4\" >61</td>\n",
       "      <td id=\"T_e8333_row4_col5\" class=\"data row4 col5\" >8</td>\n",
       "      <td id=\"T_e8333_row4_col6\" class=\"data row4 col6\" >229</td>\n",
       "      <td id=\"T_e8333_row4_col7\" class=\"data row4 col7\" >83</td>\n",
       "      <td id=\"T_e8333_row4_col8\" class=\"data row4 col8\" >162</td>\n",
       "      <td id=\"T_e8333_row4_col9\" class=\"data row4 col9\" >316</td>\n",
       "      <td id=\"T_e8333_row4_col10\" class=\"data row4 col10\" >26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e8333_level0_row5\" class=\"row_heading level0 row5\" >Phase >= 3</th>\n",
       "      <td id=\"T_e8333_row5_col0\" class=\"data row5 col0\" >0.3333</td>\n",
       "      <td id=\"T_e8333_row5_col1\" class=\"data row5 col1\" >0.6154</td>\n",
       "      <td id=\"T_e8333_row5_col2\" class=\"data row5 col2\" >0.432400</td>\n",
       "      <td id=\"T_e8333_row5_col3\" class=\"data row5 col3\" >16</td>\n",
       "      <td id=\"T_e8333_row5_col4\" class=\"data row5 col4\" >32</td>\n",
       "      <td id=\"T_e8333_row5_col5\" class=\"data row5 col5\" >10</td>\n",
       "      <td id=\"T_e8333_row5_col6\" class=\"data row5 col6\" >258</td>\n",
       "      <td id=\"T_e8333_row5_col7\" class=\"data row5 col7\" >15</td>\n",
       "      <td id=\"T_e8333_row5_col8\" class=\"data row5 col8\" >63</td>\n",
       "      <td id=\"T_e8333_row5_col9\" class=\"data row5 col9\" >316</td>\n",
       "      <td id=\"T_e8333_row5_col10\" class=\"data row5 col10\" >26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e8333_level0_row6\" class=\"row_heading level0 row6\" >Sample Size >= 5</th>\n",
       "      <td id=\"T_e8333_row6_col0\" class=\"data row6 col0\" >0.1338</td>\n",
       "      <td id=\"T_e8333_row6_col1\" class=\"data row6 col1\" >0.8077</td>\n",
       "      <td id=\"T_e8333_row6_col2\" class=\"data row6 col2\" >0.229500</td>\n",
       "      <td id=\"T_e8333_row6_col3\" class=\"data row6 col3\" >21</td>\n",
       "      <td id=\"T_e8333_row6_col4\" class=\"data row6 col4\" >136</td>\n",
       "      <td id=\"T_e8333_row6_col5\" class=\"data row6 col5\" >5</td>\n",
       "      <td id=\"T_e8333_row6_col6\" class=\"data row6 col6\" >154</td>\n",
       "      <td id=\"T_e8333_row6_col7\" class=\"data row6 col7\" >153</td>\n",
       "      <td id=\"T_e8333_row6_col8\" class=\"data row6 col8\" >310</td>\n",
       "      <td id=\"T_e8333_row6_col9\" class=\"data row6 col9\" >316</td>\n",
       "      <td id=\"T_e8333_row6_col10\" class=\"data row6 col10\" >26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e8333_level0_row7\" class=\"row_heading level0 row7\" >Sample Size >= 10</th>\n",
       "      <td id=\"T_e8333_row7_col0\" class=\"data row7 col0\" >0.1346</td>\n",
       "      <td id=\"T_e8333_row7_col1\" class=\"data row7 col1\" >0.8077</td>\n",
       "      <td id=\"T_e8333_row7_col2\" class=\"data row7 col2\" >0.230800</td>\n",
       "      <td id=\"T_e8333_row7_col3\" class=\"data row7 col3\" >21</td>\n",
       "      <td id=\"T_e8333_row7_col4\" class=\"data row7 col4\" >135</td>\n",
       "      <td id=\"T_e8333_row7_col5\" class=\"data row7 col5\" >5</td>\n",
       "      <td id=\"T_e8333_row7_col6\" class=\"data row7 col6\" >155</td>\n",
       "      <td id=\"T_e8333_row7_col7\" class=\"data row7 col7\" >150</td>\n",
       "      <td id=\"T_e8333_row7_col8\" class=\"data row7 col8\" >306</td>\n",
       "      <td id=\"T_e8333_row7_col9\" class=\"data row7 col9\" >316</td>\n",
       "      <td id=\"T_e8333_row7_col10\" class=\"data row7 col10\" >26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e8333_level0_row8\" class=\"row_heading level0 row8\" >Sample Size >= 15</th>\n",
       "      <td id=\"T_e8333_row8_col0\" class=\"data row8 col0\" >0.1364</td>\n",
       "      <td id=\"T_e8333_row8_col1\" class=\"data row8 col1\" >0.8077</td>\n",
       "      <td id=\"T_e8333_row8_col2\" class=\"data row8 col2\" >0.233300</td>\n",
       "      <td id=\"T_e8333_row8_col3\" class=\"data row8 col3\" >21</td>\n",
       "      <td id=\"T_e8333_row8_col4\" class=\"data row8 col4\" >133</td>\n",
       "      <td id=\"T_e8333_row8_col5\" class=\"data row8 col5\" >5</td>\n",
       "      <td id=\"T_e8333_row8_col6\" class=\"data row8 col6\" >157</td>\n",
       "      <td id=\"T_e8333_row8_col7\" class=\"data row8 col7\" >142</td>\n",
       "      <td id=\"T_e8333_row8_col8\" class=\"data row8 col8\" >296</td>\n",
       "      <td id=\"T_e8333_row8_col9\" class=\"data row8 col9\" >316</td>\n",
       "      <td id=\"T_e8333_row8_col10\" class=\"data row8 col10\" >26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e8333_level0_row9\" class=\"row_heading level0 row9\" >Known Intervention</th>\n",
       "      <td id=\"T_e8333_row9_col0\" class=\"data row9 col0\" >0.1606</td>\n",
       "      <td id=\"T_e8333_row9_col1\" class=\"data row9 col1\" >0.8462</td>\n",
       "      <td id=\"T_e8333_row9_col2\" class=\"data row9 col2\" >0.269900</td>\n",
       "      <td id=\"T_e8333_row9_col3\" class=\"data row9 col3\" >22</td>\n",
       "      <td id=\"T_e8333_row9_col4\" class=\"data row9 col4\" >115</td>\n",
       "      <td id=\"T_e8333_row9_col5\" class=\"data row9 col5\" >4</td>\n",
       "      <td id=\"T_e8333_row9_col6\" class=\"data row9 col6\" >175</td>\n",
       "      <td id=\"T_e8333_row9_col7\" class=\"data row9 col7\" >126</td>\n",
       "      <td id=\"T_e8333_row9_col8\" class=\"data row9 col8\" >263</td>\n",
       "      <td id=\"T_e8333_row9_col9\" class=\"data row9 col9\" >316</td>\n",
       "      <td id=\"T_e8333_row9_col10\" class=\"data row9 col10\" >26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e8333_level0_row10\" class=\"row_heading level0 row10\" >Unknown Intervention</th>\n",
       "      <td id=\"T_e8333_row10_col0\" class=\"data row10 col0\" >0.1059</td>\n",
       "      <td id=\"T_e8333_row10_col1\" class=\"data row10 col1\" >0.3462</td>\n",
       "      <td id=\"T_e8333_row10_col2\" class=\"data row10 col2\" >0.162200</td>\n",
       "      <td id=\"T_e8333_row10_col3\" class=\"data row10 col3\" >9</td>\n",
       "      <td id=\"T_e8333_row10_col4\" class=\"data row10 col4\" >76</td>\n",
       "      <td id=\"T_e8333_row10_col5\" class=\"data row10 col5\" >17</td>\n",
       "      <td id=\"T_e8333_row10_col6\" class=\"data row10 col6\" >214</td>\n",
       "      <td id=\"T_e8333_row10_col7\" class=\"data row10 col7\" >104</td>\n",
       "      <td id=\"T_e8333_row10_col8\" class=\"data row10 col8\" >189</td>\n",
       "      <td id=\"T_e8333_row10_col9\" class=\"data row10 col9\" >316</td>\n",
       "      <td id=\"T_e8333_row10_col10\" class=\"data row10 col10\" >26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e8333_level0_row11\" class=\"row_heading level0 row11\" >K+U Intervention</th>\n",
       "      <td id=\"T_e8333_row11_col0\" class=\"data row11 col0\" >0.1233</td>\n",
       "      <td id=\"T_e8333_row11_col1\" class=\"data row11 col1\" >0.3462</td>\n",
       "      <td id=\"T_e8333_row11_col2\" class=\"data row11 col2\" >0.181800</td>\n",
       "      <td id=\"T_e8333_row11_col3\" class=\"data row11 col3\" >9</td>\n",
       "      <td id=\"T_e8333_row11_col4\" class=\"data row11 col4\" >64</td>\n",
       "      <td id=\"T_e8333_row11_col5\" class=\"data row11 col5\" >17</td>\n",
       "      <td id=\"T_e8333_row11_col6\" class=\"data row11 col6\" >226</td>\n",
       "      <td id=\"T_e8333_row11_col7\" class=\"data row11 col7\" >82</td>\n",
       "      <td id=\"T_e8333_row11_col8\" class=\"data row11 col8\" >155</td>\n",
       "      <td id=\"T_e8333_row11_col9\" class=\"data row11 col9\" >316</td>\n",
       "      <td id=\"T_e8333_row11_col10\" class=\"data row11 col10\" >26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e8333_level0_row12\" class=\"row_heading level0 row12\" >Significant Finding</th>\n",
       "      <td id=\"T_e8333_row12_col0\" class=\"data row12 col0\" >0.1596</td>\n",
       "      <td id=\"T_e8333_row12_col1\" class=\"data row12 col1\" >0.5769</td>\n",
       "      <td id=\"T_e8333_row12_col2\" class=\"data row12 col2\" >0.250000</td>\n",
       "      <td id=\"T_e8333_row12_col3\" class=\"data row12 col3\" >15</td>\n",
       "      <td id=\"T_e8333_row12_col4\" class=\"data row12 col4\" >79</td>\n",
       "      <td id=\"T_e8333_row12_col5\" class=\"data row12 col5\" >11</td>\n",
       "      <td id=\"T_e8333_row12_col6\" class=\"data row12 col6\" >211</td>\n",
       "      <td id=\"T_e8333_row12_col7\" class=\"data row12 col7\" >129</td>\n",
       "      <td id=\"T_e8333_row12_col8\" class=\"data row12 col8\" >223</td>\n",
       "      <td id=\"T_e8333_row12_col9\" class=\"data row12 col9\" >316</td>\n",
       "      <td id=\"T_e8333_row12_col10\" class=\"data row12 col10\" >26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e8333_level0_row13\" class=\"row_heading level0 row13\" >No Significant Finding</th>\n",
       "      <td id=\"T_e8333_row13_col0\" class=\"data row13 col0\" >0.1029</td>\n",
       "      <td id=\"T_e8333_row13_col1\" class=\"data row13 col1\" >0.2692</td>\n",
       "      <td id=\"T_e8333_row13_col2\" class=\"data row13 col2\" >0.148900</td>\n",
       "      <td id=\"T_e8333_row13_col3\" class=\"data row13 col3\" >7</td>\n",
       "      <td id=\"T_e8333_row13_col4\" class=\"data row13 col4\" >61</td>\n",
       "      <td id=\"T_e8333_row13_col5\" class=\"data row13 col5\" >19</td>\n",
       "      <td id=\"T_e8333_row13_col6\" class=\"data row13 col6\" >229</td>\n",
       "      <td id=\"T_e8333_row13_col7\" class=\"data row13 col7\" >31</td>\n",
       "      <td id=\"T_e8333_row13_col8\" class=\"data row13 col8\" >99</td>\n",
       "      <td id=\"T_e8333_row13_col9\" class=\"data row13 col9\" >316</td>\n",
       "      <td id=\"T_e8333_row13_col10\" class=\"data row13 col10\" >26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f68ba71b8b0>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "highlight_metrics(\n",
    "    metrics_to_df(metrics[\"Only RCTs\"], drop_zero_tp=False).head(len(FILTERS_RETRIEVED))\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from evaluation.metrics import build_filter_combinations_table "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluating screened document subset 'All'\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1e0044d2dcf0427d9b6579e377cf4b02",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Evaluating filter combinations:   0%|          | 0/12 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluating screened document subset 'Only RCTs'\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0b90aa387d5f41d1ace5c1c63f64a90a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Evaluating filter combinations:   0%|          | 0/12 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_b2a8a_row0_col1, #T_b2a8a_row0_col3, #T_b2a8a_row0_col5, #T_b2a8a_row1_col1, #T_b2a8a_row1_col3, #T_b2a8a_row1_col5, #T_b2a8a_row2_col0, #T_b2a8a_row2_col2, #T_b2a8a_row2_col4 {\n",
       "  font-weight: bold;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_b2a8a\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank\" >&nbsp;</th>\n",
       "      <th class=\"blank\" >&nbsp;</th>\n",
       "      <th class=\"blank\" >&nbsp;</th>\n",
       "      <th class=\"blank\" >&nbsp;</th>\n",
       "      <th class=\"blank\" >&nbsp;</th>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_b2a8a_level0_col0\" class=\"col_heading level0 col0\" >Prec.</th>\n",
       "      <th id=\"T_b2a8a_level0_col1\" class=\"col_heading level0 col1\" >Rec.</th>\n",
       "      <th id=\"T_b2a8a_level0_col2\" class=\"col_heading level0 col2\" >$F_1$</th>\n",
       "      <th id=\"T_b2a8a_level0_col3\" class=\"col_heading level0 col3\" >TP</th>\n",
       "      <th id=\"T_b2a8a_level0_col4\" class=\"col_heading level0 col4\" >FP</th>\n",
       "      <th id=\"T_b2a8a_level0_col5\" class=\"col_heading level0 col5\" >FN</th>\n",
       "      <th id=\"T_b2a8a_level0_col6\" class=\"col_heading level0 col6\" >TN</th>\n",
       "      <th id=\"T_b2a8a_level0_col7\" class=\"col_heading level0 col7\" >?</th>\n",
       "      <th id=\"T_b2a8a_level0_col8\" class=\"col_heading level0 col8\" >Retr.</th>\n",
       "      <th id=\"T_b2a8a_level0_col9\" class=\"col_heading level0 col9\" >Scr.</th>\n",
       "      <th id=\"T_b2a8a_level0_col10\" class=\"col_heading level0 col10\" >Rel.</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_b2a8a_level0_row0\" class=\"row_heading level0 row0\" rowspan=\"3\">RCTs</th>\n",
       "      <th id=\"T_b2a8a_level1_row0\" class=\"row_heading level1 row0\" ></th>\n",
       "      <th id=\"T_b2a8a_level2_row0\" class=\"row_heading level2 row0\" ></th>\n",
       "      <th id=\"T_b2a8a_level3_row0\" class=\"row_heading level3 row0\" ></th>\n",
       "      <th id=\"T_b2a8a_level4_row0\" class=\"row_heading level4 row0\" ></th>\n",
       "      <th id=\"T_b2a8a_level5_row0\" class=\"row_heading level5 row0\" ></th>\n",
       "      <td id=\"T_b2a8a_row0_col0\" class=\"data row0 col0\" >0.135800</td>\n",
       "      <td id=\"T_b2a8a_row0_col1\" class=\"data row0 col1\" >0.846200</td>\n",
       "      <td id=\"T_b2a8a_row0_col2\" class=\"data row0 col2\" >0.234000</td>\n",
       "      <td id=\"T_b2a8a_row0_col3\" class=\"data row0 col3\" >22</td>\n",
       "      <td id=\"T_b2a8a_row0_col4\" class=\"data row0 col4\" >140</td>\n",
       "      <td id=\"T_b2a8a_row0_col5\" class=\"data row0 col5\" >4</td>\n",
       "      <td id=\"T_b2a8a_row0_col6\" class=\"data row0 col6\" >150</td>\n",
       "      <td id=\"T_b2a8a_row0_col7\" class=\"data row0 col7\" >48</td>\n",
       "      <td id=\"T_b2a8a_row0_col8\" class=\"data row0 col8\" >210</td>\n",
       "      <td id=\"T_b2a8a_row0_col9\" class=\"data row0 col9\" >316</td>\n",
       "      <td id=\"T_b2a8a_row0_col10\" class=\"data row0 col10\" >26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b2a8a_level1_row1\" class=\"row_heading level1 row1\" rowspan=\"2\">Abstr., NC</th>\n",
       "      <th id=\"T_b2a8a_level2_row1\" class=\"row_heading level2 row1\" ></th>\n",
       "      <th id=\"T_b2a8a_level3_row1\" class=\"row_heading level3 row1\" ></th>\n",
       "      <th id=\"T_b2a8a_level4_row1\" class=\"row_heading level4 row1\" ></th>\n",
       "      <th id=\"T_b2a8a_level5_row1\" class=\"row_heading level5 row1\" ></th>\n",
       "      <td id=\"T_b2a8a_row1_col0\" class=\"data row1 col0\" >0.136600</td>\n",
       "      <td id=\"T_b2a8a_row1_col1\" class=\"data row1 col1\" >0.846200</td>\n",
       "      <td id=\"T_b2a8a_row1_col2\" class=\"data row1 col2\" >0.235300</td>\n",
       "      <td id=\"T_b2a8a_row1_col3\" class=\"data row1 col3\" >22</td>\n",
       "      <td id=\"T_b2a8a_row1_col4\" class=\"data row1 col4\" >139</td>\n",
       "      <td id=\"T_b2a8a_row1_col5\" class=\"data row1 col5\" >4</td>\n",
       "      <td id=\"T_b2a8a_row1_col6\" class=\"data row1 col6\" >151</td>\n",
       "      <td id=\"T_b2a8a_row1_col7\" class=\"data row1 col7\" >47</td>\n",
       "      <td id=\"T_b2a8a_row1_col8\" class=\"data row1 col8\" >208</td>\n",
       "      <td id=\"T_b2a8a_row1_col9\" class=\"data row1 col9\" >316</td>\n",
       "      <td id=\"T_b2a8a_row1_col10\" class=\"data row1 col10\" >26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b2a8a_level2_row2\" class=\"row_heading level2 row2\" >Phase >= 2</th>\n",
       "      <th id=\"T_b2a8a_level3_row2\" class=\"row_heading level3 row2\" ></th>\n",
       "      <th id=\"T_b2a8a_level4_row2\" class=\"row_heading level4 row2\" ></th>\n",
       "      <th id=\"T_b2a8a_level5_row2\" class=\"row_heading level5 row2\" ></th>\n",
       "      <td id=\"T_b2a8a_row2_col0\" class=\"data row2 col0\" >0.227800</td>\n",
       "      <td id=\"T_b2a8a_row2_col1\" class=\"data row2 col1\" >0.692300</td>\n",
       "      <td id=\"T_b2a8a_row2_col2\" class=\"data row2 col2\" >0.342900</td>\n",
       "      <td id=\"T_b2a8a_row2_col3\" class=\"data row2 col3\" >18</td>\n",
       "      <td id=\"T_b2a8a_row2_col4\" class=\"data row2 col4\" >61</td>\n",
       "      <td id=\"T_b2a8a_row2_col5\" class=\"data row2 col5\" >8</td>\n",
       "      <td id=\"T_b2a8a_row2_col6\" class=\"data row2 col6\" >229</td>\n",
       "      <td id=\"T_b2a8a_row2_col7\" class=\"data row2 col7\" >15</td>\n",
       "      <td id=\"T_b2a8a_row2_col8\" class=\"data row2 col8\" >94</td>\n",
       "      <td id=\"T_b2a8a_row2_col9\" class=\"data row2 col9\" >316</td>\n",
       "      <td id=\"T_b2a8a_row2_col10\" class=\"data row2 col10\" >26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f68bc81f760>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combinations_table, metrics_combinations = build_filter_combinations_table(\n",
    "    df_retrieved=df_r,\n",
    "    df_screened=df_s_with_ggponc.query(\"screening_origin == 'original'\"),\n",
    "    latex_output_path=PATH_LATEX_TABLE_OUTPUT / \"final_metrics_original_only.tex\",\n",
    "    stop_at_line=3,\n",
    "    overwrite_latex_output=OVERWRITE_LATEX_OUTPUT,\n",
    "    use_abbreviations=True,\n",
    "    column_format=\"llllllS[table-format=1.2]S[table-format=1.2]S[table-format=1.2]S[table-format=2]S[table-format=3]S[table-format=1]S[table-format=3]S[table-format=2]S[table-format=3]\",\n",
    ")\n",
    "\n",
    "highlight_metrics(combinations_table)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Including Results of Manual Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluating screened document subset 'All'\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ddd23d996e414954917c293b52386d58",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Evaluating filter combinations:   0%|          | 0/14 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluating screened document subset 'Only RCTs'\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f4dde4eaf523412a9fd9c88c47433eda",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Evaluating filter combinations:   0%|          | 0/14 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "metrics_w_manual = run_evaluation(\n",
    "    df_s_with_ggponc,\n",
    "    df_r,\n",
    "    filters_screened=FILTERS_SCREENED,\n",
    "    filters_retrieved=FILTERS_RETRIEVED,\n",
    "    col_id=\"pm_id\",\n",
    "    col_relevant=\"is_relevant\",\n",
    "    compute_combinations=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### All"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 551.181x340.649 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_venn_rsi(\n",
    "    title=\"All\",\n",
    "    ids_retrieved=metrics_w_manual[\"All\"][\"All\"][\"ids_retrieved\"],\n",
    "    ids_screened=metrics_w_manual[\"All\"][\"All\"][\"ids_screened\"],\n",
    "    ids_screened_relevant=metrics_w_manual[\"All\"][\"All\"][\"ids_screened_relevant\"],\n",
    "    add_pn_labels=True,\n",
    ");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_82b86_row0_col1, #T_82b86_row0_col3, #T_82b86_row0_col5, #T_82b86_row2_col1, #T_82b86_row2_col3, #T_82b86_row2_col5, #T_82b86_row3_col1, #T_82b86_row3_col3, #T_82b86_row3_col5, #T_82b86_row5_col0, #T_82b86_row5_col2, #T_82b86_row5_col4 {\n",
       "  font-weight: bold;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_82b86\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_82b86_level0_col0\" class=\"col_heading level0 col0\" >Precision</th>\n",
       "      <th id=\"T_82b86_level0_col1\" class=\"col_heading level0 col1\" >Recall</th>\n",
       "      <th id=\"T_82b86_level0_col2\" class=\"col_heading level0 col2\" >$F_1$</th>\n",
       "      <th id=\"T_82b86_level0_col3\" class=\"col_heading level0 col3\" >TP</th>\n",
       "      <th id=\"T_82b86_level0_col4\" class=\"col_heading level0 col4\" >FP</th>\n",
       "      <th id=\"T_82b86_level0_col5\" class=\"col_heading level0 col5\" >FN</th>\n",
       "      <th id=\"T_82b86_level0_col6\" class=\"col_heading level0 col6\" >TN</th>\n",
       "      <th id=\"T_82b86_level0_col7\" class=\"col_heading level0 col7\" >?</th>\n",
       "      <th id=\"T_82b86_level0_col8\" class=\"col_heading level0 col8\" >Retrieved</th>\n",
       "      <th id=\"T_82b86_level0_col9\" class=\"col_heading level0 col9\" >Screened</th>\n",
       "      <th id=\"T_82b86_level0_col10\" class=\"col_heading level0 col10\" >Relevant</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_82b86_level0_row0\" class=\"row_heading level0 row0\" >All</th>\n",
       "      <td id=\"T_82b86_row0_col0\" class=\"data row0 col0\" >0.1331</td>\n",
       "      <td id=\"T_82b86_row0_col1\" class=\"data row0 col1\" >0.3667</td>\n",
       "      <td id=\"T_82b86_row0_col2\" class=\"data row0 col2\" >0.195300</td>\n",
       "      <td id=\"T_82b86_row0_col3\" class=\"data row0 col3\" >33</td>\n",
       "      <td id=\"T_82b86_row0_col4\" class=\"data row0 col4\" >215</td>\n",
       "      <td id=\"T_82b86_row0_col5\" class=\"data row0 col5\" >57</td>\n",
       "      <td id=\"T_82b86_row0_col6\" class=\"data row0 col6\" >1876</td>\n",
       "      <td id=\"T_82b86_row0_col7\" class=\"data row0 col7\" >78</td>\n",
       "      <td id=\"T_82b86_row0_col8\" class=\"data row0 col8\" >326</td>\n",
       "      <td id=\"T_82b86_row0_col9\" class=\"data row0 col9\" >2181</td>\n",
       "      <td id=\"T_82b86_row0_col10\" class=\"data row0 col10\" >90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_82b86_level0_row1\" class=\"row_heading level0 row1\" >Is RCT</th>\n",
       "      <td id=\"T_82b86_row1_col0\" class=\"data row1 col0\" >0.1751</td>\n",
       "      <td id=\"T_82b86_row1_col1\" class=\"data row1 col1\" >0.3444</td>\n",
       "      <td id=\"T_82b86_row1_col2\" class=\"data row1 col2\" >0.232200</td>\n",
       "      <td id=\"T_82b86_row1_col3\" class=\"data row1 col3\" >31</td>\n",
       "      <td id=\"T_82b86_row1_col4\" class=\"data row1 col4\" >146</td>\n",
       "      <td id=\"T_82b86_row1_col5\" class=\"data row1 col5\" >59</td>\n",
       "      <td id=\"T_82b86_row1_col6\" class=\"data row1 col6\" >1945</td>\n",
       "      <td id=\"T_82b86_row1_col7\" class=\"data row1 col7\" >33</td>\n",
       "      <td id=\"T_82b86_row1_col8\" class=\"data row1 col8\" >210</td>\n",
       "      <td id=\"T_82b86_row1_col9\" class=\"data row1 col9\" >2181</td>\n",
       "      <td id=\"T_82b86_row1_col10\" class=\"data row1 col10\" >90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_82b86_level0_row2\" class=\"row_heading level0 row2\" >Non-empty Abstract</th>\n",
       "      <td id=\"T_82b86_row2_col0\" class=\"data row2 col0\" >0.1331</td>\n",
       "      <td id=\"T_82b86_row2_col1\" class=\"data row2 col1\" >0.3667</td>\n",
       "      <td id=\"T_82b86_row2_col2\" class=\"data row2 col2\" >0.195300</td>\n",
       "      <td id=\"T_82b86_row2_col3\" class=\"data row2 col3\" >33</td>\n",
       "      <td id=\"T_82b86_row2_col4\" class=\"data row2 col4\" >215</td>\n",
       "      <td id=\"T_82b86_row2_col5\" class=\"data row2 col5\" >57</td>\n",
       "      <td id=\"T_82b86_row2_col6\" class=\"data row2 col6\" >1876</td>\n",
       "      <td id=\"T_82b86_row2_col7\" class=\"data row2 col7\" >74</td>\n",
       "      <td id=\"T_82b86_row2_col8\" class=\"data row2 col8\" >322</td>\n",
       "      <td id=\"T_82b86_row2_col9\" class=\"data row2 col9\" >2181</td>\n",
       "      <td id=\"T_82b86_row2_col10\" class=\"data row2 col10\" >90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_82b86_level0_row3\" class=\"row_heading level0 row3\" >Non-pediatric population</th>\n",
       "      <td id=\"T_82b86_row3_col0\" class=\"data row3 col0\" >0.1336</td>\n",
       "      <td id=\"T_82b86_row3_col1\" class=\"data row3 col1\" >0.3667</td>\n",
       "      <td id=\"T_82b86_row3_col2\" class=\"data row3 col2\" >0.195800</td>\n",
       "      <td id=\"T_82b86_row3_col3\" class=\"data row3 col3\" >33</td>\n",
       "      <td id=\"T_82b86_row3_col4\" class=\"data row3 col4\" >214</td>\n",
       "      <td id=\"T_82b86_row3_col5\" class=\"data row3 col5\" >57</td>\n",
       "      <td id=\"T_82b86_row3_col6\" class=\"data row3 col6\" >1877</td>\n",
       "      <td id=\"T_82b86_row3_col7\" class=\"data row3 col7\" >78</td>\n",
       "      <td id=\"T_82b86_row3_col8\" class=\"data row3 col8\" >325</td>\n",
       "      <td id=\"T_82b86_row3_col9\" class=\"data row3 col9\" >2181</td>\n",
       "      <td id=\"T_82b86_row3_col10\" class=\"data row3 col10\" >90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_82b86_level0_row4\" class=\"row_heading level0 row4\" >Phase >= 2</th>\n",
       "      <td id=\"T_82b86_row4_col0\" class=\"data row4 col0\" >0.2057</td>\n",
       "      <td id=\"T_82b86_row4_col1\" class=\"data row4 col1\" >0.3222</td>\n",
       "      <td id=\"T_82b86_row4_col2\" class=\"data row4 col2\" >0.251100</td>\n",
       "      <td id=\"T_82b86_row4_col3\" class=\"data row4 col3\" >29</td>\n",
       "      <td id=\"T_82b86_row4_col4\" class=\"data row4 col4\" >112</td>\n",
       "      <td id=\"T_82b86_row4_col5\" class=\"data row4 col5\" >61</td>\n",
       "      <td id=\"T_82b86_row4_col6\" class=\"data row4 col6\" >1979</td>\n",
       "      <td id=\"T_82b86_row4_col7\" class=\"data row4 col7\" >21</td>\n",
       "      <td id=\"T_82b86_row4_col8\" class=\"data row4 col8\" >162</td>\n",
       "      <td id=\"T_82b86_row4_col9\" class=\"data row4 col9\" >2181</td>\n",
       "      <td id=\"T_82b86_row4_col10\" class=\"data row4 col10\" >90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_82b86_level0_row5\" class=\"row_heading level0 row5\" >Phase >= 3</th>\n",
       "      <td id=\"T_82b86_row5_col0\" class=\"data row5 col0\" >0.3387</td>\n",
       "      <td id=\"T_82b86_row5_col1\" class=\"data row5 col1\" >0.2333</td>\n",
       "      <td id=\"T_82b86_row5_col2\" class=\"data row5 col2\" >0.276300</td>\n",
       "      <td id=\"T_82b86_row5_col3\" class=\"data row5 col3\" >21</td>\n",
       "      <td id=\"T_82b86_row5_col4\" class=\"data row5 col4\" >41</td>\n",
       "      <td id=\"T_82b86_row5_col5\" class=\"data row5 col5\" >69</td>\n",
       "      <td id=\"T_82b86_row5_col6\" class=\"data row5 col6\" >2050</td>\n",
       "      <td id=\"T_82b86_row5_col7\" class=\"data row5 col7\" >1</td>\n",
       "      <td id=\"T_82b86_row5_col8\" class=\"data row5 col8\" >63</td>\n",
       "      <td id=\"T_82b86_row5_col9\" class=\"data row5 col9\" >2181</td>\n",
       "      <td id=\"T_82b86_row5_col10\" class=\"data row5 col10\" >90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_82b86_level0_row6\" class=\"row_heading level0 row6\" >Sample Size >= 5</th>\n",
       "      <td id=\"T_82b86_row6_col0\" class=\"data row6 col0\" >0.1308</td>\n",
       "      <td id=\"T_82b86_row6_col1\" class=\"data row6 col1\" >0.3444</td>\n",
       "      <td id=\"T_82b86_row6_col2\" class=\"data row6 col2\" >0.189600</td>\n",
       "      <td id=\"T_82b86_row6_col3\" class=\"data row6 col3\" >31</td>\n",
       "      <td id=\"T_82b86_row6_col4\" class=\"data row6 col4\" >206</td>\n",
       "      <td id=\"T_82b86_row6_col5\" class=\"data row6 col5\" >59</td>\n",
       "      <td id=\"T_82b86_row6_col6\" class=\"data row6 col6\" >1885</td>\n",
       "      <td id=\"T_82b86_row6_col7\" class=\"data row6 col7\" >73</td>\n",
       "      <td id=\"T_82b86_row6_col8\" class=\"data row6 col8\" >310</td>\n",
       "      <td id=\"T_82b86_row6_col9\" class=\"data row6 col9\" >2181</td>\n",
       "      <td id=\"T_82b86_row6_col10\" class=\"data row6 col10\" >90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_82b86_level0_row7\" class=\"row_heading level0 row7\" >Sample Size >= 10</th>\n",
       "      <td id=\"T_82b86_row7_col0\" class=\"data row7 col0\" >0.1325</td>\n",
       "      <td id=\"T_82b86_row7_col1\" class=\"data row7 col1\" >0.3444</td>\n",
       "      <td id=\"T_82b86_row7_col2\" class=\"data row7 col2\" >0.191400</td>\n",
       "      <td id=\"T_82b86_row7_col3\" class=\"data row7 col3\" >31</td>\n",
       "      <td id=\"T_82b86_row7_col4\" class=\"data row7 col4\" >203</td>\n",
       "      <td id=\"T_82b86_row7_col5\" class=\"data row7 col5\" >59</td>\n",
       "      <td id=\"T_82b86_row7_col6\" class=\"data row7 col6\" >1888</td>\n",
       "      <td id=\"T_82b86_row7_col7\" class=\"data row7 col7\" >72</td>\n",
       "      <td id=\"T_82b86_row7_col8\" class=\"data row7 col8\" >306</td>\n",
       "      <td id=\"T_82b86_row7_col9\" class=\"data row7 col9\" >2181</td>\n",
       "      <td id=\"T_82b86_row7_col10\" class=\"data row7 col10\" >90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_82b86_level0_row8\" class=\"row_heading level0 row8\" >Sample Size >= 15</th>\n",
       "      <td id=\"T_82b86_row8_col0\" class=\"data row8 col0\" >0.1354</td>\n",
       "      <td id=\"T_82b86_row8_col1\" class=\"data row8 col1\" >0.3444</td>\n",
       "      <td id=\"T_82b86_row8_col2\" class=\"data row8 col2\" >0.194400</td>\n",
       "      <td id=\"T_82b86_row8_col3\" class=\"data row8 col3\" >31</td>\n",
       "      <td id=\"T_82b86_row8_col4\" class=\"data row8 col4\" >198</td>\n",
       "      <td id=\"T_82b86_row8_col5\" class=\"data row8 col5\" >59</td>\n",
       "      <td id=\"T_82b86_row8_col6\" class=\"data row8 col6\" >1893</td>\n",
       "      <td id=\"T_82b86_row8_col7\" class=\"data row8 col7\" >67</td>\n",
       "      <td id=\"T_82b86_row8_col8\" class=\"data row8 col8\" >296</td>\n",
       "      <td id=\"T_82b86_row8_col9\" class=\"data row8 col9\" >2181</td>\n",
       "      <td id=\"T_82b86_row8_col10\" class=\"data row8 col10\" >90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_82b86_level0_row9\" class=\"row_heading level0 row9\" >Known Intervention</th>\n",
       "      <td id=\"T_82b86_row9_col0\" class=\"data row9 col0\" >0.1509</td>\n",
       "      <td id=\"T_82b86_row9_col1\" class=\"data row9 col1\" >0.3556</td>\n",
       "      <td id=\"T_82b86_row9_col2\" class=\"data row9 col2\" >0.211900</td>\n",
       "      <td id=\"T_82b86_row9_col3\" class=\"data row9 col3\" >32</td>\n",
       "      <td id=\"T_82b86_row9_col4\" class=\"data row9 col4\" >180</td>\n",
       "      <td id=\"T_82b86_row9_col5\" class=\"data row9 col5\" >58</td>\n",
       "      <td id=\"T_82b86_row9_col6\" class=\"data row9 col6\" >1911</td>\n",
       "      <td id=\"T_82b86_row9_col7\" class=\"data row9 col7\" >51</td>\n",
       "      <td id=\"T_82b86_row9_col8\" class=\"data row9 col8\" >263</td>\n",
       "      <td id=\"T_82b86_row9_col9\" class=\"data row9 col9\" >2181</td>\n",
       "      <td id=\"T_82b86_row9_col10\" class=\"data row9 col10\" >90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_82b86_level0_row10\" class=\"row_heading level0 row10\" >Unknown Intervention</th>\n",
       "      <td id=\"T_82b86_row10_col0\" class=\"data row10 col0\" >0.1103</td>\n",
       "      <td id=\"T_82b86_row10_col1\" class=\"data row10 col1\" >0.1778</td>\n",
       "      <td id=\"T_82b86_row10_col2\" class=\"data row10 col2\" >0.136200</td>\n",
       "      <td id=\"T_82b86_row10_col3\" class=\"data row10 col3\" >16</td>\n",
       "      <td id=\"T_82b86_row10_col4\" class=\"data row10 col4\" >129</td>\n",
       "      <td id=\"T_82b86_row10_col5\" class=\"data row10 col5\" >74</td>\n",
       "      <td id=\"T_82b86_row10_col6\" class=\"data row10 col6\" >1962</td>\n",
       "      <td id=\"T_82b86_row10_col7\" class=\"data row10 col7\" >44</td>\n",
       "      <td id=\"T_82b86_row10_col8\" class=\"data row10 col8\" >189</td>\n",
       "      <td id=\"T_82b86_row10_col9\" class=\"data row10 col9\" >2181</td>\n",
       "      <td id=\"T_82b86_row10_col10\" class=\"data row10 col10\" >90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_82b86_level0_row11\" class=\"row_heading level0 row11\" >K+U Intervention</th>\n",
       "      <td id=\"T_82b86_row11_col0\" class=\"data row11 col0\" >0.1210</td>\n",
       "      <td id=\"T_82b86_row11_col1\" class=\"data row11 col1\" >0.1667</td>\n",
       "      <td id=\"T_82b86_row11_col2\" class=\"data row11 col2\" >0.140200</td>\n",
       "      <td id=\"T_82b86_row11_col3\" class=\"data row11 col3\" >15</td>\n",
       "      <td id=\"T_82b86_row11_col4\" class=\"data row11 col4\" >109</td>\n",
       "      <td id=\"T_82b86_row11_col5\" class=\"data row11 col5\" >75</td>\n",
       "      <td id=\"T_82b86_row11_col6\" class=\"data row11 col6\" >1982</td>\n",
       "      <td id=\"T_82b86_row11_col7\" class=\"data row11 col7\" >31</td>\n",
       "      <td id=\"T_82b86_row11_col8\" class=\"data row11 col8\" >155</td>\n",
       "      <td id=\"T_82b86_row11_col9\" class=\"data row11 col9\" >2181</td>\n",
       "      <td id=\"T_82b86_row11_col10\" class=\"data row11 col10\" >90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_82b86_level0_row12\" class=\"row_heading level0 row12\" >Significant Finding</th>\n",
       "      <td id=\"T_82b86_row12_col0\" class=\"data row12 col0\" >0.1198</td>\n",
       "      <td id=\"T_82b86_row12_col1\" class=\"data row12 col1\" >0.2222</td>\n",
       "      <td id=\"T_82b86_row12_col2\" class=\"data row12 col2\" >0.155600</td>\n",
       "      <td id=\"T_82b86_row12_col3\" class=\"data row12 col3\" >20</td>\n",
       "      <td id=\"T_82b86_row12_col4\" class=\"data row12 col4\" >147</td>\n",
       "      <td id=\"T_82b86_row12_col5\" class=\"data row12 col5\" >70</td>\n",
       "      <td id=\"T_82b86_row12_col6\" class=\"data row12 col6\" >1944</td>\n",
       "      <td id=\"T_82b86_row12_col7\" class=\"data row12 col7\" >56</td>\n",
       "      <td id=\"T_82b86_row12_col8\" class=\"data row12 col8\" >223</td>\n",
       "      <td id=\"T_82b86_row12_col9\" class=\"data row12 col9\" >2181</td>\n",
       "      <td id=\"T_82b86_row12_col10\" class=\"data row12 col10\" >90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_82b86_level0_row13\" class=\"row_heading level0 row13\" >No Significant Finding</th>\n",
       "      <td id=\"T_82b86_row13_col0\" class=\"data row13 col0\" >0.1605</td>\n",
       "      <td id=\"T_82b86_row13_col1\" class=\"data row13 col1\" >0.1444</td>\n",
       "      <td id=\"T_82b86_row13_col2\" class=\"data row13 col2\" >0.152000</td>\n",
       "      <td id=\"T_82b86_row13_col3\" class=\"data row13 col3\" >13</td>\n",
       "      <td id=\"T_82b86_row13_col4\" class=\"data row13 col4\" >68</td>\n",
       "      <td id=\"T_82b86_row13_col5\" class=\"data row13 col5\" >77</td>\n",
       "      <td id=\"T_82b86_row13_col6\" class=\"data row13 col6\" >2023</td>\n",
       "      <td id=\"T_82b86_row13_col7\" class=\"data row13 col7\" >18</td>\n",
       "      <td id=\"T_82b86_row13_col8\" class=\"data row13 col8\" >99</td>\n",
       "      <td id=\"T_82b86_row13_col9\" class=\"data row13 col9\" >2181</td>\n",
       "      <td id=\"T_82b86_row13_col10\" class=\"data row13 col10\" >90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f68ba7db220>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "highlight_metrics(\n",
    "    metrics_to_df(metrics_w_manual[\"All\"], drop_zero_tp=False).head(\n",
    "        len(FILTERS_RETRIEVED)\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### RCTs only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 551.181x340.649 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_venn_rsi(\n",
    "    title=\"RCTs only\",\n",
    "    ids_retrieved=metrics_w_manual[\"Only RCTs\"][\"Is RCT\"][\"ids_retrieved\"],\n",
    "    ids_screened=metrics_w_manual[\"Only RCTs\"][\"Is RCT\"][\"ids_screened\"],\n",
    "    ids_screened_relevant=metrics_w_manual[\"Only RCTs\"][\"Is RCT\"][\n",
    "        \"ids_screened_relevant\"\n",
    "    ],\n",
    "    add_pn_labels=True,\n",
    ");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_7a1a1_row0_col1, #T_7a1a1_row0_col3, #T_7a1a1_row0_col5, #T_7a1a1_row1_col1, #T_7a1a1_row1_col3, #T_7a1a1_row1_col5, #T_7a1a1_row2_col1, #T_7a1a1_row2_col3, #T_7a1a1_row2_col5, #T_7a1a1_row3_col1, #T_7a1a1_row3_col3, #T_7a1a1_row3_col5, #T_7a1a1_row5_col0, #T_7a1a1_row5_col2, #T_7a1a1_row5_col4 {\n",
       "  font-weight: bold;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_7a1a1\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_7a1a1_level0_col0\" class=\"col_heading level0 col0\" >Precision</th>\n",
       "      <th id=\"T_7a1a1_level0_col1\" class=\"col_heading level0 col1\" >Recall</th>\n",
       "      <th id=\"T_7a1a1_level0_col2\" class=\"col_heading level0 col2\" >$F_1$</th>\n",
       "      <th id=\"T_7a1a1_level0_col3\" class=\"col_heading level0 col3\" >TP</th>\n",
       "      <th id=\"T_7a1a1_level0_col4\" class=\"col_heading level0 col4\" >FP</th>\n",
       "      <th id=\"T_7a1a1_level0_col5\" class=\"col_heading level0 col5\" >FN</th>\n",
       "      <th id=\"T_7a1a1_level0_col6\" class=\"col_heading level0 col6\" >TN</th>\n",
       "      <th id=\"T_7a1a1_level0_col7\" class=\"col_heading level0 col7\" >?</th>\n",
       "      <th id=\"T_7a1a1_level0_col8\" class=\"col_heading level0 col8\" >Retrieved</th>\n",
       "      <th id=\"T_7a1a1_level0_col9\" class=\"col_heading level0 col9\" >Screened</th>\n",
       "      <th id=\"T_7a1a1_level0_col10\" class=\"col_heading level0 col10\" >Relevant</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_7a1a1_level0_row0\" class=\"row_heading level0 row0\" >All</th>\n",
       "      <td id=\"T_7a1a1_row0_col0\" class=\"data row0 col0\" >0.1751</td>\n",
       "      <td id=\"T_7a1a1_row0_col1\" class=\"data row0 col1\" >0.8857</td>\n",
       "      <td id=\"T_7a1a1_row0_col2\" class=\"data row0 col2\" >0.292500</td>\n",
       "      <td id=\"T_7a1a1_row0_col3\" class=\"data row0 col3\" >31</td>\n",
       "      <td id=\"T_7a1a1_row0_col4\" class=\"data row0 col4\" >146</td>\n",
       "      <td id=\"T_7a1a1_row0_col5\" class=\"data row0 col5\" >4</td>\n",
       "      <td id=\"T_7a1a1_row0_col6\" class=\"data row0 col6\" >150</td>\n",
       "      <td id=\"T_7a1a1_row0_col7\" class=\"data row0 col7\" >149</td>\n",
       "      <td id=\"T_7a1a1_row0_col8\" class=\"data row0 col8\" >326</td>\n",
       "      <td id=\"T_7a1a1_row0_col9\" class=\"data row0 col9\" >331</td>\n",
       "      <td id=\"T_7a1a1_row0_col10\" class=\"data row0 col10\" >35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7a1a1_level0_row1\" class=\"row_heading level0 row1\" >Is RCT</th>\n",
       "      <td id=\"T_7a1a1_row1_col0\" class=\"data row1 col0\" >0.1751</td>\n",
       "      <td id=\"T_7a1a1_row1_col1\" class=\"data row1 col1\" >0.8857</td>\n",
       "      <td id=\"T_7a1a1_row1_col2\" class=\"data row1 col2\" >0.292500</td>\n",
       "      <td id=\"T_7a1a1_row1_col3\" class=\"data row1 col3\" >31</td>\n",
       "      <td id=\"T_7a1a1_row1_col4\" class=\"data row1 col4\" >146</td>\n",
       "      <td id=\"T_7a1a1_row1_col5\" class=\"data row1 col5\" >4</td>\n",
       "      <td id=\"T_7a1a1_row1_col6\" class=\"data row1 col6\" >150</td>\n",
       "      <td id=\"T_7a1a1_row1_col7\" class=\"data row1 col7\" >33</td>\n",
       "      <td id=\"T_7a1a1_row1_col8\" class=\"data row1 col8\" >210</td>\n",
       "      <td id=\"T_7a1a1_row1_col9\" class=\"data row1 col9\" >331</td>\n",
       "      <td id=\"T_7a1a1_row1_col10\" class=\"data row1 col10\" >35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7a1a1_level0_row2\" class=\"row_heading level0 row2\" >Non-empty Abstract</th>\n",
       "      <td id=\"T_7a1a1_row2_col0\" class=\"data row2 col0\" >0.1751</td>\n",
       "      <td id=\"T_7a1a1_row2_col1\" class=\"data row2 col1\" >0.8857</td>\n",
       "      <td id=\"T_7a1a1_row2_col2\" class=\"data row2 col2\" >0.292500</td>\n",
       "      <td id=\"T_7a1a1_row2_col3\" class=\"data row2 col3\" >31</td>\n",
       "      <td id=\"T_7a1a1_row2_col4\" class=\"data row2 col4\" >146</td>\n",
       "      <td id=\"T_7a1a1_row2_col5\" class=\"data row2 col5\" >4</td>\n",
       "      <td id=\"T_7a1a1_row2_col6\" class=\"data row2 col6\" >150</td>\n",
       "      <td id=\"T_7a1a1_row2_col7\" class=\"data row2 col7\" >145</td>\n",
       "      <td id=\"T_7a1a1_row2_col8\" class=\"data row2 col8\" >322</td>\n",
       "      <td id=\"T_7a1a1_row2_col9\" class=\"data row2 col9\" >331</td>\n",
       "      <td id=\"T_7a1a1_row2_col10\" class=\"data row2 col10\" >35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7a1a1_level0_row3\" class=\"row_heading level0 row3\" >Non-pediatric population</th>\n",
       "      <td id=\"T_7a1a1_row3_col0\" class=\"data row3 col0\" >0.1761</td>\n",
       "      <td id=\"T_7a1a1_row3_col1\" class=\"data row3 col1\" >0.8857</td>\n",
       "      <td id=\"T_7a1a1_row3_col2\" class=\"data row3 col2\" >0.293800</td>\n",
       "      <td id=\"T_7a1a1_row3_col3\" class=\"data row3 col3\" >31</td>\n",
       "      <td id=\"T_7a1a1_row3_col4\" class=\"data row3 col4\" >145</td>\n",
       "      <td id=\"T_7a1a1_row3_col5\" class=\"data row3 col5\" >4</td>\n",
       "      <td id=\"T_7a1a1_row3_col6\" class=\"data row3 col6\" >151</td>\n",
       "      <td id=\"T_7a1a1_row3_col7\" class=\"data row3 col7\" >149</td>\n",
       "      <td id=\"T_7a1a1_row3_col8\" class=\"data row3 col8\" >325</td>\n",
       "      <td id=\"T_7a1a1_row3_col9\" class=\"data row3 col9\" >331</td>\n",
       "      <td id=\"T_7a1a1_row3_col10\" class=\"data row3 col10\" >35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7a1a1_level0_row4\" class=\"row_heading level0 row4\" >Phase >= 2</th>\n",
       "      <td id=\"T_7a1a1_row4_col0\" class=\"data row4 col0\" >0.2872</td>\n",
       "      <td id=\"T_7a1a1_row4_col1\" class=\"data row4 col1\" >0.7714</td>\n",
       "      <td id=\"T_7a1a1_row4_col2\" class=\"data row4 col2\" >0.418600</td>\n",
       "      <td id=\"T_7a1a1_row4_col3\" class=\"data row4 col3\" >27</td>\n",
       "      <td id=\"T_7a1a1_row4_col4\" class=\"data row4 col4\" >67</td>\n",
       "      <td id=\"T_7a1a1_row4_col5\" class=\"data row4 col5\" >8</td>\n",
       "      <td id=\"T_7a1a1_row4_col6\" class=\"data row4 col6\" >229</td>\n",
       "      <td id=\"T_7a1a1_row4_col7\" class=\"data row4 col7\" >68</td>\n",
       "      <td id=\"T_7a1a1_row4_col8\" class=\"data row4 col8\" >162</td>\n",
       "      <td id=\"T_7a1a1_row4_col9\" class=\"data row4 col9\" >331</td>\n",
       "      <td id=\"T_7a1a1_row4_col10\" class=\"data row4 col10\" >35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7a1a1_level0_row5\" class=\"row_heading level0 row5\" >Phase >= 3</th>\n",
       "      <td id=\"T_7a1a1_row5_col0\" class=\"data row5 col0\" >0.3636</td>\n",
       "      <td id=\"T_7a1a1_row5_col1\" class=\"data row5 col1\" >0.5714</td>\n",
       "      <td id=\"T_7a1a1_row5_col2\" class=\"data row5 col2\" >0.444400</td>\n",
       "      <td id=\"T_7a1a1_row5_col3\" class=\"data row5 col3\" >20</td>\n",
       "      <td id=\"T_7a1a1_row5_col4\" class=\"data row5 col4\" >35</td>\n",
       "      <td id=\"T_7a1a1_row5_col5\" class=\"data row5 col5\" >15</td>\n",
       "      <td id=\"T_7a1a1_row5_col6\" class=\"data row5 col6\" >261</td>\n",
       "      <td id=\"T_7a1a1_row5_col7\" class=\"data row5 col7\" >8</td>\n",
       "      <td id=\"T_7a1a1_row5_col8\" class=\"data row5 col8\" >63</td>\n",
       "      <td id=\"T_7a1a1_row5_col9\" class=\"data row5 col9\" >331</td>\n",
       "      <td id=\"T_7a1a1_row5_col10\" class=\"data row5 col10\" >35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7a1a1_level0_row6\" class=\"row_heading level0 row6\" >Sample Size >= 5</th>\n",
       "      <td id=\"T_7a1a1_row6_col0\" class=\"data row6 col0\" >0.1696</td>\n",
       "      <td id=\"T_7a1a1_row6_col1\" class=\"data row6 col1\" >0.8286</td>\n",
       "      <td id=\"T_7a1a1_row6_col2\" class=\"data row6 col2\" >0.281600</td>\n",
       "      <td id=\"T_7a1a1_row6_col3\" class=\"data row6 col3\" >29</td>\n",
       "      <td id=\"T_7a1a1_row6_col4\" class=\"data row6 col4\" >142</td>\n",
       "      <td id=\"T_7a1a1_row6_col5\" class=\"data row6 col5\" >6</td>\n",
       "      <td id=\"T_7a1a1_row6_col6\" class=\"data row6 col6\" >154</td>\n",
       "      <td id=\"T_7a1a1_row6_col7\" class=\"data row6 col7\" >139</td>\n",
       "      <td id=\"T_7a1a1_row6_col8\" class=\"data row6 col8\" >310</td>\n",
       "      <td id=\"T_7a1a1_row6_col9\" class=\"data row6 col9\" >331</td>\n",
       "      <td id=\"T_7a1a1_row6_col10\" class=\"data row6 col10\" >35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7a1a1_level0_row7\" class=\"row_heading level0 row7\" >Sample Size >= 10</th>\n",
       "      <td id=\"T_7a1a1_row7_col0\" class=\"data row7 col0\" >0.1706</td>\n",
       "      <td id=\"T_7a1a1_row7_col1\" class=\"data row7 col1\" >0.8286</td>\n",
       "      <td id=\"T_7a1a1_row7_col2\" class=\"data row7 col2\" >0.282900</td>\n",
       "      <td id=\"T_7a1a1_row7_col3\" class=\"data row7 col3\" >29</td>\n",
       "      <td id=\"T_7a1a1_row7_col4\" class=\"data row7 col4\" >141</td>\n",
       "      <td id=\"T_7a1a1_row7_col5\" class=\"data row7 col5\" >6</td>\n",
       "      <td id=\"T_7a1a1_row7_col6\" class=\"data row7 col6\" >155</td>\n",
       "      <td id=\"T_7a1a1_row7_col7\" class=\"data row7 col7\" >136</td>\n",
       "      <td id=\"T_7a1a1_row7_col8\" class=\"data row7 col8\" >306</td>\n",
       "      <td id=\"T_7a1a1_row7_col9\" class=\"data row7 col9\" >331</td>\n",
       "      <td id=\"T_7a1a1_row7_col10\" class=\"data row7 col10\" >35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7a1a1_level0_row8\" class=\"row_heading level0 row8\" >Sample Size >= 15</th>\n",
       "      <td id=\"T_7a1a1_row8_col0\" class=\"data row8 col0\" >0.1737</td>\n",
       "      <td id=\"T_7a1a1_row8_col1\" class=\"data row8 col1\" >0.8286</td>\n",
       "      <td id=\"T_7a1a1_row8_col2\" class=\"data row8 col2\" >0.287100</td>\n",
       "      <td id=\"T_7a1a1_row8_col3\" class=\"data row8 col3\" >29</td>\n",
       "      <td id=\"T_7a1a1_row8_col4\" class=\"data row8 col4\" >138</td>\n",
       "      <td id=\"T_7a1a1_row8_col5\" class=\"data row8 col5\" >6</td>\n",
       "      <td id=\"T_7a1a1_row8_col6\" class=\"data row8 col6\" >158</td>\n",
       "      <td id=\"T_7a1a1_row8_col7\" class=\"data row8 col7\" >129</td>\n",
       "      <td id=\"T_7a1a1_row8_col8\" class=\"data row8 col8\" >296</td>\n",
       "      <td id=\"T_7a1a1_row8_col9\" class=\"data row8 col9\" >331</td>\n",
       "      <td id=\"T_7a1a1_row8_col10\" class=\"data row8 col10\" >35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7a1a1_level0_row9\" class=\"row_heading level0 row9\" >Known Intervention</th>\n",
       "      <td id=\"T_7a1a1_row9_col0\" class=\"data row9 col0\" >0.1987</td>\n",
       "      <td id=\"T_7a1a1_row9_col1\" class=\"data row9 col1\" >0.8571</td>\n",
       "      <td id=\"T_7a1a1_row9_col2\" class=\"data row9 col2\" >0.322600</td>\n",
       "      <td id=\"T_7a1a1_row9_col3\" class=\"data row9 col3\" >30</td>\n",
       "      <td id=\"T_7a1a1_row9_col4\" class=\"data row9 col4\" >121</td>\n",
       "      <td id=\"T_7a1a1_row9_col5\" class=\"data row9 col5\" >5</td>\n",
       "      <td id=\"T_7a1a1_row9_col6\" class=\"data row9 col6\" >175</td>\n",
       "      <td id=\"T_7a1a1_row9_col7\" class=\"data row9 col7\" >112</td>\n",
       "      <td id=\"T_7a1a1_row9_col8\" class=\"data row9 col8\" >263</td>\n",
       "      <td id=\"T_7a1a1_row9_col9\" class=\"data row9 col9\" >331</td>\n",
       "      <td id=\"T_7a1a1_row9_col10\" class=\"data row9 col10\" >35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7a1a1_level0_row10\" class=\"row_heading level0 row10\" >Unknown Intervention</th>\n",
       "      <td id=\"T_7a1a1_row10_col0\" class=\"data row10 col0\" >0.1546</td>\n",
       "      <td id=\"T_7a1a1_row10_col1\" class=\"data row10 col1\" >0.4286</td>\n",
       "      <td id=\"T_7a1a1_row10_col2\" class=\"data row10 col2\" >0.227300</td>\n",
       "      <td id=\"T_7a1a1_row10_col3\" class=\"data row10 col3\" >15</td>\n",
       "      <td id=\"T_7a1a1_row10_col4\" class=\"data row10 col4\" >82</td>\n",
       "      <td id=\"T_7a1a1_row10_col5\" class=\"data row10 col5\" >20</td>\n",
       "      <td id=\"T_7a1a1_row10_col6\" class=\"data row10 col6\" >214</td>\n",
       "      <td id=\"T_7a1a1_row10_col7\" class=\"data row10 col7\" >92</td>\n",
       "      <td id=\"T_7a1a1_row10_col8\" class=\"data row10 col8\" >189</td>\n",
       "      <td id=\"T_7a1a1_row10_col9\" class=\"data row10 col9\" >331</td>\n",
       "      <td id=\"T_7a1a1_row10_col10\" class=\"data row10 col10\" >35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7a1a1_level0_row11\" class=\"row_heading level0 row11\" >K+U Intervention</th>\n",
       "      <td id=\"T_7a1a1_row11_col0\" class=\"data row11 col0\" >0.1667</td>\n",
       "      <td id=\"T_7a1a1_row11_col1\" class=\"data row11 col1\" >0.4000</td>\n",
       "      <td id=\"T_7a1a1_row11_col2\" class=\"data row11 col2\" >0.235300</td>\n",
       "      <td id=\"T_7a1a1_row11_col3\" class=\"data row11 col3\" >14</td>\n",
       "      <td id=\"T_7a1a1_row11_col4\" class=\"data row11 col4\" >70</td>\n",
       "      <td id=\"T_7a1a1_row11_col5\" class=\"data row11 col5\" >21</td>\n",
       "      <td id=\"T_7a1a1_row11_col6\" class=\"data row11 col6\" >226</td>\n",
       "      <td id=\"T_7a1a1_row11_col7\" class=\"data row11 col7\" >71</td>\n",
       "      <td id=\"T_7a1a1_row11_col8\" class=\"data row11 col8\" >155</td>\n",
       "      <td id=\"T_7a1a1_row11_col9\" class=\"data row11 col9\" >331</td>\n",
       "      <td id=\"T_7a1a1_row11_col10\" class=\"data row11 col10\" >35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7a1a1_level0_row12\" class=\"row_heading level0 row12\" >Significant Finding</th>\n",
       "      <td id=\"T_7a1a1_row12_col0\" class=\"data row12 col0\" >0.1845</td>\n",
       "      <td id=\"T_7a1a1_row12_col1\" class=\"data row12 col1\" >0.5429</td>\n",
       "      <td id=\"T_7a1a1_row12_col2\" class=\"data row12 col2\" >0.275400</td>\n",
       "      <td id=\"T_7a1a1_row12_col3\" class=\"data row12 col3\" >19</td>\n",
       "      <td id=\"T_7a1a1_row12_col4\" class=\"data row12 col4\" >84</td>\n",
       "      <td id=\"T_7a1a1_row12_col5\" class=\"data row12 col5\" >16</td>\n",
       "      <td id=\"T_7a1a1_row12_col6\" class=\"data row12 col6\" >212</td>\n",
       "      <td id=\"T_7a1a1_row12_col7\" class=\"data row12 col7\" >120</td>\n",
       "      <td id=\"T_7a1a1_row12_col8\" class=\"data row12 col8\" >223</td>\n",
       "      <td id=\"T_7a1a1_row12_col9\" class=\"data row12 col9\" >331</td>\n",
       "      <td id=\"T_7a1a1_row12_col10\" class=\"data row12 col10\" >35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7a1a1_level0_row13\" class=\"row_heading level0 row13\" >No Significant Finding</th>\n",
       "      <td id=\"T_7a1a1_row13_col0\" class=\"data row13 col0\" >0.1622</td>\n",
       "      <td id=\"T_7a1a1_row13_col1\" class=\"data row13 col1\" >0.3429</td>\n",
       "      <td id=\"T_7a1a1_row13_col2\" class=\"data row13 col2\" >0.220200</td>\n",
       "      <td id=\"T_7a1a1_row13_col3\" class=\"data row13 col3\" >12</td>\n",
       "      <td id=\"T_7a1a1_row13_col4\" class=\"data row13 col4\" >62</td>\n",
       "      <td id=\"T_7a1a1_row13_col5\" class=\"data row13 col5\" >23</td>\n",
       "      <td id=\"T_7a1a1_row13_col6\" class=\"data row13 col6\" >234</td>\n",
       "      <td id=\"T_7a1a1_row13_col7\" class=\"data row13 col7\" >25</td>\n",
       "      <td id=\"T_7a1a1_row13_col8\" class=\"data row13 col8\" >99</td>\n",
       "      <td id=\"T_7a1a1_row13_col9\" class=\"data row13 col9\" >331</td>\n",
       "      <td id=\"T_7a1a1_row13_col10\" class=\"data row13 col10\" >35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f68ba7dae00>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "highlight_metrics(\n",
    "    metrics_to_df(metrics_w_manual[\"Only RCTs\"], drop_zero_tp=False).head(\n",
    "        len(FILTERS_RETRIEVED)\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Final Results (for RCTs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from evaluation.metrics import build_filter_combinations_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Queries for combinations table\n",
    "Q_RCTS = \"is_rct_pt == True & abstract.str.len() > 1\"\n",
    "Q_RCTS_P2 = Q_RCTS + \" & phase_int >= 2\"\n",
    "Q_RCTS_P3 = Q_RCTS + \" & phase_int >= 3\"\n",
    "Q_RCTS_P3_NC = Q_RCTS_P3 + \" & has_pediatric_population == False\"\n",
    "Q_SIG = \"(has_significant_finding == True)\"\n",
    "Q_INSIQ = \"(has_significant_finding == False)\"\n",
    "Q_KNOWN = \"(has_known_intervention == True)\"\n",
    "Q_UNKNOWN = \"(has_unknown_intervention == True)\"\n",
    "Q_REC = \"(has_recommended_intervention == True)\"\n",
    "Q_UNREC = \"(has_not_recommended_intervention == True)\"\n",
    "\n",
    "# Filters for combinations table\n",
    "FILTERS_COMBINATIONS = {\n",
    "    \"RCTs\": Q_RCTS,\n",
    "    \"Phase >= 2\": Q_RCTS_P2,\n",
    "    \"Phase >= 3\": Q_RCTS_P3,\n",
    "    \"Exclude Children\": Q_RCTS_P3_NC,\n",
    "    \n",
    "    \"Significant\" : Q_RCTS_P3_NC + \"&\" + Q_SIG,\n",
    "    \n",
    "    \"R\" :  Q_RCTS_P3_NC + \"&\" + Q_REC + \"&\" + Q_SIG,\n",
    "    \"NR\" :  Q_RCTS_P3_NC + \"&\" + Q_UNREC + \"&\" + Q_SIG,\n",
    "}\n",
    "\n",
    "# Hierarchy for combinations table\n",
    "FILTERS_HIERARCHY = {\n",
    "    \"RCTs\": {\n",
    "        \"Phase >= 2\": {\n",
    "            \"Phase >= 3\": {\n",
    "                \"Exclude Children\": \n",
    "                    {\n",
    "                        \"Significant\" : [\"R\", \"NR\" ],\n",
    "                    }\n",
    "                }\n",
    "            }\n",
    "        }\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Without manual review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluating screened document subset 'All'\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "290f59e8b1894d96a453b0d0c54e84bb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Evaluating filter combinations:   0%|          | 0/7 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluating screened document subset 'Only RCTs'\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "11b05c8c05764d6ab47e25f493196963",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Evaluating filter combinations:   0%|          | 0/7 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_22ff7_row0_col1, #T_22ff7_row0_col3, #T_22ff7_row0_col5, #T_22ff7_row5_col0, #T_22ff7_row5_col2, #T_22ff7_row6_col4 {\n",
       "  font-weight: bold;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_22ff7\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank\" >&nbsp;</th>\n",
       "      <th class=\"blank\" >&nbsp;</th>\n",
       "      <th class=\"blank\" >&nbsp;</th>\n",
       "      <th class=\"blank\" >&nbsp;</th>\n",
       "      <th class=\"blank\" >&nbsp;</th>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_22ff7_level0_col0\" class=\"col_heading level0 col0\" >Prec.</th>\n",
       "      <th id=\"T_22ff7_level0_col1\" class=\"col_heading level0 col1\" >Rec.</th>\n",
       "      <th id=\"T_22ff7_level0_col2\" class=\"col_heading level0 col2\" >$F_1$</th>\n",
       "      <th id=\"T_22ff7_level0_col3\" class=\"col_heading level0 col3\" >TP</th>\n",
       "      <th id=\"T_22ff7_level0_col4\" class=\"col_heading level0 col4\" >FP</th>\n",
       "      <th id=\"T_22ff7_level0_col5\" class=\"col_heading level0 col5\" >FN</th>\n",
       "      <th id=\"T_22ff7_level0_col6\" class=\"col_heading level0 col6\" >TN</th>\n",
       "      <th id=\"T_22ff7_level0_col7\" class=\"col_heading level0 col7\" >?</th>\n",
       "      <th id=\"T_22ff7_level0_col8\" class=\"col_heading level0 col8\" >Retr.</th>\n",
       "      <th id=\"T_22ff7_level0_col9\" class=\"col_heading level0 col9\" >Scr.</th>\n",
       "      <th id=\"T_22ff7_level0_col10\" class=\"col_heading level0 col10\" >Rel.</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_22ff7_level0_row0\" class=\"row_heading level0 row0\" rowspan=\"7\">RCTs</th>\n",
       "      <th id=\"T_22ff7_level1_row0\" class=\"row_heading level1 row0\" ></th>\n",
       "      <th id=\"T_22ff7_level2_row0\" class=\"row_heading level2 row0\" ></th>\n",
       "      <th id=\"T_22ff7_level3_row0\" class=\"row_heading level3 row0\" ></th>\n",
       "      <th id=\"T_22ff7_level4_row0\" class=\"row_heading level4 row0\" ></th>\n",
       "      <th id=\"T_22ff7_level5_row0\" class=\"row_heading level5 row0\" ></th>\n",
       "      <td id=\"T_22ff7_row0_col0\" class=\"data row0 col0\" >0.135800</td>\n",
       "      <td id=\"T_22ff7_row0_col1\" class=\"data row0 col1\" >0.846200</td>\n",
       "      <td id=\"T_22ff7_row0_col2\" class=\"data row0 col2\" >0.234000</td>\n",
       "      <td id=\"T_22ff7_row0_col3\" class=\"data row0 col3\" >22</td>\n",
       "      <td id=\"T_22ff7_row0_col4\" class=\"data row0 col4\" >140</td>\n",
       "      <td id=\"T_22ff7_row0_col5\" class=\"data row0 col5\" >4</td>\n",
       "      <td id=\"T_22ff7_row0_col6\" class=\"data row0 col6\" >150</td>\n",
       "      <td id=\"T_22ff7_row0_col7\" class=\"data row0 col7\" >47</td>\n",
       "      <td id=\"T_22ff7_row0_col8\" class=\"data row0 col8\" >209</td>\n",
       "      <td id=\"T_22ff7_row0_col9\" class=\"data row0 col9\" >316</td>\n",
       "      <td id=\"T_22ff7_row0_col10\" class=\"data row0 col10\" >26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_22ff7_level1_row1\" class=\"row_heading level1 row1\" rowspan=\"6\">Phase >= 2</th>\n",
       "      <th id=\"T_22ff7_level2_row1\" class=\"row_heading level2 row1\" ></th>\n",
       "      <th id=\"T_22ff7_level3_row1\" class=\"row_heading level3 row1\" ></th>\n",
       "      <th id=\"T_22ff7_level4_row1\" class=\"row_heading level4 row1\" ></th>\n",
       "      <th id=\"T_22ff7_level5_row1\" class=\"row_heading level5 row1\" ></th>\n",
       "      <td id=\"T_22ff7_row1_col0\" class=\"data row1 col0\" >0.227800</td>\n",
       "      <td id=\"T_22ff7_row1_col1\" class=\"data row1 col1\" >0.692300</td>\n",
       "      <td id=\"T_22ff7_row1_col2\" class=\"data row1 col2\" >0.342900</td>\n",
       "      <td id=\"T_22ff7_row1_col3\" class=\"data row1 col3\" >18</td>\n",
       "      <td id=\"T_22ff7_row1_col4\" class=\"data row1 col4\" >61</td>\n",
       "      <td id=\"T_22ff7_row1_col5\" class=\"data row1 col5\" >8</td>\n",
       "      <td id=\"T_22ff7_row1_col6\" class=\"data row1 col6\" >229</td>\n",
       "      <td id=\"T_22ff7_row1_col7\" class=\"data row1 col7\" >15</td>\n",
       "      <td id=\"T_22ff7_row1_col8\" class=\"data row1 col8\" >94</td>\n",
       "      <td id=\"T_22ff7_row1_col9\" class=\"data row1 col9\" >316</td>\n",
       "      <td id=\"T_22ff7_row1_col10\" class=\"data row1 col10\" >26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_22ff7_level2_row2\" class=\"row_heading level2 row2\" rowspan=\"5\">Phase >= 3</th>\n",
       "      <th id=\"T_22ff7_level3_row2\" class=\"row_heading level3 row2\" ></th>\n",
       "      <th id=\"T_22ff7_level4_row2\" class=\"row_heading level4 row2\" ></th>\n",
       "      <th id=\"T_22ff7_level5_row2\" class=\"row_heading level5 row2\" ></th>\n",
       "      <td id=\"T_22ff7_row2_col0\" class=\"data row2 col0\" >0.333300</td>\n",
       "      <td id=\"T_22ff7_row2_col1\" class=\"data row2 col1\" >0.615400</td>\n",
       "      <td id=\"T_22ff7_row2_col2\" class=\"data row2 col2\" >0.432400</td>\n",
       "      <td id=\"T_22ff7_row2_col3\" class=\"data row2 col3\" >16</td>\n",
       "      <td id=\"T_22ff7_row2_col4\" class=\"data row2 col4\" >32</td>\n",
       "      <td id=\"T_22ff7_row2_col5\" class=\"data row2 col5\" >10</td>\n",
       "      <td id=\"T_22ff7_row2_col6\" class=\"data row2 col6\" >258</td>\n",
       "      <td id=\"T_22ff7_row2_col7\" class=\"data row2 col7\" >7</td>\n",
       "      <td id=\"T_22ff7_row2_col8\" class=\"data row2 col8\" >55</td>\n",
       "      <td id=\"T_22ff7_row2_col9\" class=\"data row2 col9\" >316</td>\n",
       "      <td id=\"T_22ff7_row2_col10\" class=\"data row2 col10\" >26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_22ff7_level3_row3\" class=\"row_heading level3 row3\" rowspan=\"4\">Exclude Children</th>\n",
       "      <th id=\"T_22ff7_level4_row3\" class=\"row_heading level4 row3\" ></th>\n",
       "      <th id=\"T_22ff7_level5_row3\" class=\"row_heading level5 row3\" ></th>\n",
       "      <td id=\"T_22ff7_row3_col0\" class=\"data row3 col0\" >0.333300</td>\n",
       "      <td id=\"T_22ff7_row3_col1\" class=\"data row3 col1\" >0.615400</td>\n",
       "      <td id=\"T_22ff7_row3_col2\" class=\"data row3 col2\" >0.432400</td>\n",
       "      <td id=\"T_22ff7_row3_col3\" class=\"data row3 col3\" >16</td>\n",
       "      <td id=\"T_22ff7_row3_col4\" class=\"data row3 col4\" >32</td>\n",
       "      <td id=\"T_22ff7_row3_col5\" class=\"data row3 col5\" >10</td>\n",
       "      <td id=\"T_22ff7_row3_col6\" class=\"data row3 col6\" >258</td>\n",
       "      <td id=\"T_22ff7_row3_col7\" class=\"data row3 col7\" >7</td>\n",
       "      <td id=\"T_22ff7_row3_col8\" class=\"data row3 col8\" >55</td>\n",
       "      <td id=\"T_22ff7_row3_col9\" class=\"data row3 col9\" >316</td>\n",
       "      <td id=\"T_22ff7_row3_col10\" class=\"data row3 col10\" >26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_22ff7_level4_row4\" class=\"row_heading level4 row4\" rowspan=\"3\">Significant</th>\n",
       "      <th id=\"T_22ff7_level5_row4\" class=\"row_heading level5 row4\" ></th>\n",
       "      <td id=\"T_22ff7_row4_col0\" class=\"data row4 col0\" >0.406200</td>\n",
       "      <td id=\"T_22ff7_row4_col1\" class=\"data row4 col1\" >0.500000</td>\n",
       "      <td id=\"T_22ff7_row4_col2\" class=\"data row4 col2\" >0.448300</td>\n",
       "      <td id=\"T_22ff7_row4_col3\" class=\"data row4 col3\" >13</td>\n",
       "      <td id=\"T_22ff7_row4_col4\" class=\"data row4 col4\" >19</td>\n",
       "      <td id=\"T_22ff7_row4_col5\" class=\"data row4 col5\" >13</td>\n",
       "      <td id=\"T_22ff7_row4_col6\" class=\"data row4 col6\" >271</td>\n",
       "      <td id=\"T_22ff7_row4_col7\" class=\"data row4 col7\" >5</td>\n",
       "      <td id=\"T_22ff7_row4_col8\" class=\"data row4 col8\" >37</td>\n",
       "      <td id=\"T_22ff7_row4_col9\" class=\"data row4 col9\" >316</td>\n",
       "      <td id=\"T_22ff7_row4_col10\" class=\"data row4 col10\" >26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_22ff7_level5_row5\" class=\"row_heading level5 row5\" >R</th>\n",
       "      <td id=\"T_22ff7_row5_col0\" class=\"data row5 col0\" >0.464300</td>\n",
       "      <td id=\"T_22ff7_row5_col1\" class=\"data row5 col1\" >0.500000</td>\n",
       "      <td id=\"T_22ff7_row5_col2\" class=\"data row5 col2\" >0.481500</td>\n",
       "      <td id=\"T_22ff7_row5_col3\" class=\"data row5 col3\" >13</td>\n",
       "      <td id=\"T_22ff7_row5_col4\" class=\"data row5 col4\" >15</td>\n",
       "      <td id=\"T_22ff7_row5_col5\" class=\"data row5 col5\" >13</td>\n",
       "      <td id=\"T_22ff7_row5_col6\" class=\"data row5 col6\" >275</td>\n",
       "      <td id=\"T_22ff7_row5_col7\" class=\"data row5 col7\" >3</td>\n",
       "      <td id=\"T_22ff7_row5_col8\" class=\"data row5 col8\" >31</td>\n",
       "      <td id=\"T_22ff7_row5_col9\" class=\"data row5 col9\" >316</td>\n",
       "      <td id=\"T_22ff7_row5_col10\" class=\"data row5 col10\" >26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_22ff7_level5_row6\" class=\"row_heading level5 row6\" >NR</th>\n",
       "      <td id=\"T_22ff7_row6_col0\" class=\"data row6 col0\" >0.434800</td>\n",
       "      <td id=\"T_22ff7_row6_col1\" class=\"data row6 col1\" >0.384600</td>\n",
       "      <td id=\"T_22ff7_row6_col2\" class=\"data row6 col2\" >0.408200</td>\n",
       "      <td id=\"T_22ff7_row6_col3\" class=\"data row6 col3\" >10</td>\n",
       "      <td id=\"T_22ff7_row6_col4\" class=\"data row6 col4\" >13</td>\n",
       "      <td id=\"T_22ff7_row6_col5\" class=\"data row6 col5\" >16</td>\n",
       "      <td id=\"T_22ff7_row6_col6\" class=\"data row6 col6\" >277</td>\n",
       "      <td id=\"T_22ff7_row6_col7\" class=\"data row6 col7\" >5</td>\n",
       "      <td id=\"T_22ff7_row6_col8\" class=\"data row6 col8\" >28</td>\n",
       "      <td id=\"T_22ff7_row6_col9\" class=\"data row6 col9\" >316</td>\n",
       "      <td id=\"T_22ff7_row6_col10\" class=\"data row6 col10\" >26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f68ba7b2710>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combinations_table, metrics_combinations_final = build_filter_combinations_table(\n",
    "    df_retrieved=df_r,\n",
    "    df_screened=df_s_with_ggponc.query(\"screening_origin != 'manual' and screening_origin != 'ggponc'\"),\n",
    "    latex_output_path=PATH_LATEX_TABLE_OUTPUT / \"final_metrics.tex\",\n",
    "    overwrite_latex_output=False,\n",
    "    use_abbreviations=True,\n",
    "    column_format=\"llllllS[table-format=1.2]S[table-format=1.2]S[table-format=1.2]S[table-format=2]S[table-format=2]S[table-format=2]S[table-format=2]S[table-format=2]S[table-format=2]\",\n",
    "    filters_retrieved = FILTERS_COMBINATIONS,\n",
    "    filters_retrieved_hierarchy = FILTERS_HIERARCHY,\n",
    ")\n",
    "highlight_metrics(combinations_table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{lrrrrrrr}\n",
      "\\toprule\n",
      "{} &  Retr. &  TP &   ? &   FP &   TN &  FN &  Rec. \\\\\n",
      "\\midrule\n",
      "0 &    209 &  22 &  47 &  140 &  150 &   4 &  0.85 \\\\\n",
      "1 &     94 &  18 &  15 &   61 &  229 &   8 &  0.69 \\\\\n",
      "2 &     55 &  16 &   7 &   32 &  258 &  10 &  0.62 \\\\\n",
      "3 &     55 &  16 &   7 &   32 &  258 &  10 &  0.62 \\\\\n",
      "4 &     37 &  13 &   5 &   19 &  271 &  13 &  0.50 \\\\\n",
      "5 &     31 &  13 &   3 &   15 &  275 &  13 &  0.50 \\\\\n",
      "6 &     28 &  10 &   5 &   13 &  277 &  16 &  0.38 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3057356/569929526.py:1: FutureWarning: In future versions `DataFrame.to_latex` is expected to utilise the base implementation of `Styler.to_latex` for formatting and rendering. The arguments signature may therefore change. It is recommended instead to use `DataFrame.style.to_latex` which also contains additional functionality.\n",
      "  print(combinations_table.reset_index()[['Retr.', 'TP', '?', 'FP', 'TN', 'FN', 'Rec.']].round(2).to_latex())\n"
     ]
    }
   ],
   "source": [
    "print(combinations_table.reset_index()[['Retr.', 'TP', '?', 'FP', 'TN', 'FN', 'Rec.']].round(2).to_latex())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## After manual review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_manual = pd.read_csv(PATH_MANUAL_REVIEW)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False Nicht Teil der PICO Fragen - Immuntherapie + Chemo, Toripalimab nicht explizit gesucht \n",
      "False Nicht Teil der PICO Fragen -  Ernährung als Intervention\n",
      "False Nicht Teil der PICO Fragen - keine Dosisvergleiche gesucht\n",
      "False Nicht Teil der PICO Fragen - keine Genexpressionsstudien gesucht\n",
      "False narrativer Review, ausgeschlossen\n",
      "False Gastric Cancer als Population nicht passend\n",
      "True Durvalumab nicht explizit gesucht weil nicht im PICO Schema, grundsätzlich relevant\n",
      "True Unklar ob mit oder ohne OP. Pembrolizumab nicht explizit gesucht weil nicht im PICO Schema, grundsätzlich relevant\n",
      "True Sintilimab nicht explizit gesucht weil nicht im PICO Schema, grundsätzlich relevant\n",
      "True Trifluridine nicht explizit gesucht weil nicht im PICO Schema, grundsätzlich relevant\n",
      "True Anlotinib nicht explizit gesucht weil nicht im PICO Schema, grundsätzlich relevant\n",
      "True Erlotinib nicht explizit gesucht weil nicht im PICO Schema, grundsätzlich relevant\n",
      "True Grundsätzlich relevant\n",
      "True Grundsätzlich relevant\n",
      "True Grundsätzlich relevant\n"
     ]
    }
   ],
   "source": [
    "for _, p in df_manual[df_manual.pm_id.isin(df_r.pm_id)].sort_values('is_included').iterrows():\n",
    "    print(p.is_included, p['Ein- / Ausschlussgrund'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluating screened document subset 'All'\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2a9cbfa4eff8466c822747d57e231f9f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Evaluating filter combinations:   0%|          | 0/7 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluating screened document subset 'Only RCTs'\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ddaa300320514329b56cd5ffee164519",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Evaluating filter combinations:   0%|          | 0/7 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_dac91_row0_col1, #T_dac91_row0_col3, #T_dac91_row0_col5, #T_dac91_row2_col2, #T_dac91_row3_col2, #T_dac91_row5_col0, #T_dac91_row6_col4 {\n",
       "  font-weight: bold;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_dac91\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank\" >&nbsp;</th>\n",
       "      <th class=\"blank\" >&nbsp;</th>\n",
       "      <th class=\"blank\" >&nbsp;</th>\n",
       "      <th class=\"blank\" >&nbsp;</th>\n",
       "      <th class=\"blank\" >&nbsp;</th>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_dac91_level0_col0\" class=\"col_heading level0 col0\" >Prec.</th>\n",
       "      <th id=\"T_dac91_level0_col1\" class=\"col_heading level0 col1\" >Rec.</th>\n",
       "      <th id=\"T_dac91_level0_col2\" class=\"col_heading level0 col2\" >$F_1$</th>\n",
       "      <th id=\"T_dac91_level0_col3\" class=\"col_heading level0 col3\" >TP</th>\n",
       "      <th id=\"T_dac91_level0_col4\" class=\"col_heading level0 col4\" >FP</th>\n",
       "      <th id=\"T_dac91_level0_col5\" class=\"col_heading level0 col5\" >FN</th>\n",
       "      <th id=\"T_dac91_level0_col6\" class=\"col_heading level0 col6\" >TN</th>\n",
       "      <th id=\"T_dac91_level0_col7\" class=\"col_heading level0 col7\" >?</th>\n",
       "      <th id=\"T_dac91_level0_col8\" class=\"col_heading level0 col8\" >Retr.</th>\n",
       "      <th id=\"T_dac91_level0_col9\" class=\"col_heading level0 col9\" >Scr.</th>\n",
       "      <th id=\"T_dac91_level0_col10\" class=\"col_heading level0 col10\" >Rel.</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_dac91_level0_row0\" class=\"row_heading level0 row0\" rowspan=\"7\">RCTs</th>\n",
       "      <th id=\"T_dac91_level1_row0\" class=\"row_heading level1 row0\" ></th>\n",
       "      <th id=\"T_dac91_level2_row0\" class=\"row_heading level2 row0\" ></th>\n",
       "      <th id=\"T_dac91_level3_row0\" class=\"row_heading level3 row0\" ></th>\n",
       "      <th id=\"T_dac91_level4_row0\" class=\"row_heading level4 row0\" ></th>\n",
       "      <th id=\"T_dac91_level5_row0\" class=\"row_heading level5 row0\" ></th>\n",
       "      <td id=\"T_dac91_row0_col0\" class=\"data row0 col0\" >0.175100</td>\n",
       "      <td id=\"T_dac91_row0_col1\" class=\"data row0 col1\" >0.885700</td>\n",
       "      <td id=\"T_dac91_row0_col2\" class=\"data row0 col2\" >0.292500</td>\n",
       "      <td id=\"T_dac91_row0_col3\" class=\"data row0 col3\" >31</td>\n",
       "      <td id=\"T_dac91_row0_col4\" class=\"data row0 col4\" >146</td>\n",
       "      <td id=\"T_dac91_row0_col5\" class=\"data row0 col5\" >4</td>\n",
       "      <td id=\"T_dac91_row0_col6\" class=\"data row0 col6\" >150</td>\n",
       "      <td id=\"T_dac91_row0_col7\" class=\"data row0 col7\" >32</td>\n",
       "      <td id=\"T_dac91_row0_col8\" class=\"data row0 col8\" >209</td>\n",
       "      <td id=\"T_dac91_row0_col9\" class=\"data row0 col9\" >331</td>\n",
       "      <td id=\"T_dac91_row0_col10\" class=\"data row0 col10\" >35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_dac91_level1_row1\" class=\"row_heading level1 row1\" rowspan=\"6\">Phase >= 2</th>\n",
       "      <th id=\"T_dac91_level2_row1\" class=\"row_heading level2 row1\" ></th>\n",
       "      <th id=\"T_dac91_level3_row1\" class=\"row_heading level3 row1\" ></th>\n",
       "      <th id=\"T_dac91_level4_row1\" class=\"row_heading level4 row1\" ></th>\n",
       "      <th id=\"T_dac91_level5_row1\" class=\"row_heading level5 row1\" ></th>\n",
       "      <td id=\"T_dac91_row1_col0\" class=\"data row1 col0\" >0.287200</td>\n",
       "      <td id=\"T_dac91_row1_col1\" class=\"data row1 col1\" >0.771400</td>\n",
       "      <td id=\"T_dac91_row1_col2\" class=\"data row1 col2\" >0.418600</td>\n",
       "      <td id=\"T_dac91_row1_col3\" class=\"data row1 col3\" >27</td>\n",
       "      <td id=\"T_dac91_row1_col4\" class=\"data row1 col4\" >67</td>\n",
       "      <td id=\"T_dac91_row1_col5\" class=\"data row1 col5\" >8</td>\n",
       "      <td id=\"T_dac91_row1_col6\" class=\"data row1 col6\" >229</td>\n",
       "      <td id=\"T_dac91_row1_col7\" class=\"data row1 col7\" >0</td>\n",
       "      <td id=\"T_dac91_row1_col8\" class=\"data row1 col8\" >94</td>\n",
       "      <td id=\"T_dac91_row1_col9\" class=\"data row1 col9\" >331</td>\n",
       "      <td id=\"T_dac91_row1_col10\" class=\"data row1 col10\" >35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_dac91_level2_row2\" class=\"row_heading level2 row2\" rowspan=\"5\">Phase >= 3</th>\n",
       "      <th id=\"T_dac91_level3_row2\" class=\"row_heading level3 row2\" ></th>\n",
       "      <th id=\"T_dac91_level4_row2\" class=\"row_heading level4 row2\" ></th>\n",
       "      <th id=\"T_dac91_level5_row2\" class=\"row_heading level5 row2\" ></th>\n",
       "      <td id=\"T_dac91_row2_col0\" class=\"data row2 col0\" >0.363600</td>\n",
       "      <td id=\"T_dac91_row2_col1\" class=\"data row2 col1\" >0.571400</td>\n",
       "      <td id=\"T_dac91_row2_col2\" class=\"data row2 col2\" >0.444400</td>\n",
       "      <td id=\"T_dac91_row2_col3\" class=\"data row2 col3\" >20</td>\n",
       "      <td id=\"T_dac91_row2_col4\" class=\"data row2 col4\" >35</td>\n",
       "      <td id=\"T_dac91_row2_col5\" class=\"data row2 col5\" >15</td>\n",
       "      <td id=\"T_dac91_row2_col6\" class=\"data row2 col6\" >261</td>\n",
       "      <td id=\"T_dac91_row2_col7\" class=\"data row2 col7\" >0</td>\n",
       "      <td id=\"T_dac91_row2_col8\" class=\"data row2 col8\" >55</td>\n",
       "      <td id=\"T_dac91_row2_col9\" class=\"data row2 col9\" >331</td>\n",
       "      <td id=\"T_dac91_row2_col10\" class=\"data row2 col10\" >35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_dac91_level3_row3\" class=\"row_heading level3 row3\" rowspan=\"4\">Exclude Children</th>\n",
       "      <th id=\"T_dac91_level4_row3\" class=\"row_heading level4 row3\" ></th>\n",
       "      <th id=\"T_dac91_level5_row3\" class=\"row_heading level5 row3\" ></th>\n",
       "      <td id=\"T_dac91_row3_col0\" class=\"data row3 col0\" >0.363600</td>\n",
       "      <td id=\"T_dac91_row3_col1\" class=\"data row3 col1\" >0.571400</td>\n",
       "      <td id=\"T_dac91_row3_col2\" class=\"data row3 col2\" >0.444400</td>\n",
       "      <td id=\"T_dac91_row3_col3\" class=\"data row3 col3\" >20</td>\n",
       "      <td id=\"T_dac91_row3_col4\" class=\"data row3 col4\" >35</td>\n",
       "      <td id=\"T_dac91_row3_col5\" class=\"data row3 col5\" >15</td>\n",
       "      <td id=\"T_dac91_row3_col6\" class=\"data row3 col6\" >261</td>\n",
       "      <td id=\"T_dac91_row3_col7\" class=\"data row3 col7\" >0</td>\n",
       "      <td id=\"T_dac91_row3_col8\" class=\"data row3 col8\" >55</td>\n",
       "      <td id=\"T_dac91_row3_col9\" class=\"data row3 col9\" >331</td>\n",
       "      <td id=\"T_dac91_row3_col10\" class=\"data row3 col10\" >35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_dac91_level4_row4\" class=\"row_heading level4 row4\" rowspan=\"3\">Significant</th>\n",
       "      <th id=\"T_dac91_level5_row4\" class=\"row_heading level5 row4\" ></th>\n",
       "      <td id=\"T_dac91_row4_col0\" class=\"data row4 col0\" >0.405400</td>\n",
       "      <td id=\"T_dac91_row4_col1\" class=\"data row4 col1\" >0.428600</td>\n",
       "      <td id=\"T_dac91_row4_col2\" class=\"data row4 col2\" >0.416700</td>\n",
       "      <td id=\"T_dac91_row4_col3\" class=\"data row4 col3\" >15</td>\n",
       "      <td id=\"T_dac91_row4_col4\" class=\"data row4 col4\" >22</td>\n",
       "      <td id=\"T_dac91_row4_col5\" class=\"data row4 col5\" >20</td>\n",
       "      <td id=\"T_dac91_row4_col6\" class=\"data row4 col6\" >274</td>\n",
       "      <td id=\"T_dac91_row4_col7\" class=\"data row4 col7\" >0</td>\n",
       "      <td id=\"T_dac91_row4_col8\" class=\"data row4 col8\" >37</td>\n",
       "      <td id=\"T_dac91_row4_col9\" class=\"data row4 col9\" >331</td>\n",
       "      <td id=\"T_dac91_row4_col10\" class=\"data row4 col10\" >35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_dac91_level5_row5\" class=\"row_heading level5 row5\" >R</th>\n",
       "      <td id=\"T_dac91_row5_col0\" class=\"data row5 col0\" >0.451600</td>\n",
       "      <td id=\"T_dac91_row5_col1\" class=\"data row5 col1\" >0.400000</td>\n",
       "      <td id=\"T_dac91_row5_col2\" class=\"data row5 col2\" >0.424200</td>\n",
       "      <td id=\"T_dac91_row5_col3\" class=\"data row5 col3\" >14</td>\n",
       "      <td id=\"T_dac91_row5_col4\" class=\"data row5 col4\" >17</td>\n",
       "      <td id=\"T_dac91_row5_col5\" class=\"data row5 col5\" >21</td>\n",
       "      <td id=\"T_dac91_row5_col6\" class=\"data row5 col6\" >279</td>\n",
       "      <td id=\"T_dac91_row5_col7\" class=\"data row5 col7\" >0</td>\n",
       "      <td id=\"T_dac91_row5_col8\" class=\"data row5 col8\" >31</td>\n",
       "      <td id=\"T_dac91_row5_col9\" class=\"data row5 col9\" >331</td>\n",
       "      <td id=\"T_dac91_row5_col10\" class=\"data row5 col10\" >35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_dac91_level5_row6\" class=\"row_heading level5 row6\" >NR</th>\n",
       "      <td id=\"T_dac91_row6_col0\" class=\"data row6 col0\" >0.428600</td>\n",
       "      <td id=\"T_dac91_row6_col1\" class=\"data row6 col1\" >0.342900</td>\n",
       "      <td id=\"T_dac91_row6_col2\" class=\"data row6 col2\" >0.381000</td>\n",
       "      <td id=\"T_dac91_row6_col3\" class=\"data row6 col3\" >12</td>\n",
       "      <td id=\"T_dac91_row6_col4\" class=\"data row6 col4\" >16</td>\n",
       "      <td id=\"T_dac91_row6_col5\" class=\"data row6 col5\" >23</td>\n",
       "      <td id=\"T_dac91_row6_col6\" class=\"data row6 col6\" >280</td>\n",
       "      <td id=\"T_dac91_row6_col7\" class=\"data row6 col7\" >0</td>\n",
       "      <td id=\"T_dac91_row6_col8\" class=\"data row6 col8\" >28</td>\n",
       "      <td id=\"T_dac91_row6_col9\" class=\"data row6 col9\" >331</td>\n",
       "      <td id=\"T_dac91_row6_col10\" class=\"data row6 col10\" >35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f68ba7180d0>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combinations_table, metrics_combinations_final = build_filter_combinations_table(\n",
    "    df_retrieved=df_r,\n",
    "    df_screened=df_s_with_ggponc,\n",
    "    latex_output_path=None,#PATH_LATEX_TABLE_OUTPUT / \"final_metrics.tex\",\n",
    "    overwrite_latex_output=False,\n",
    "    use_abbreviations=True,\n",
    "    column_format=\"llllllS[table-format=1.2]S[table-format=1.2]S[table-format=1.2]S[table-format=2]S[table-format=3]S[table-format=2]S[table-format=3]S[table-format=3]S[table-format=3]\",\n",
    "    filters_retrieved = FILTERS_COMBINATIONS,\n",
    "    filters_retrieved_hierarchy = FILTERS_HIERARCHY,\n",
    ")\n",
    "\n",
    "highlight_metrics(combinations_table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{lrrrrrrrrr}\n",
      "\\toprule\n",
      "{} &  Retr. &  TP &   ? &   FP &   TN &  FN &  Prec. &  Rec. &  \\$F\\_1\\$ \\\\\n",
      "\\midrule\n",
      "0 &    209 &  31 &  32 &  146 &  150 &   4 &   0.18 &  0.89 &   0.29 \\\\\n",
      "1 &     94 &  27 &   0 &   67 &  229 &   8 &   0.29 &  0.77 &   0.42 \\\\\n",
      "2 &     55 &  20 &   0 &   35 &  261 &  15 &   0.36 &  0.57 &   0.44 \\\\\n",
      "3 &     55 &  20 &   0 &   35 &  261 &  15 &   0.36 &  0.57 &   0.44 \\\\\n",
      "4 &     37 &  15 &   0 &   22 &  274 &  20 &   0.41 &  0.43 &   0.42 \\\\\n",
      "5 &     31 &  14 &   0 &   17 &  279 &  21 &   0.45 &  0.40 &   0.42 \\\\\n",
      "6 &     28 &  12 &   0 &   16 &  280 &  23 &   0.43 &  0.34 &   0.38 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3057356/1384292062.py:1: FutureWarning: In future versions `DataFrame.to_latex` is expected to utilise the base implementation of `Styler.to_latex` for formatting and rendering. The arguments signature may therefore change. It is recommended instead to use `DataFrame.style.to_latex` which also contains additional functionality.\n",
      "  print(combinations_table.reset_index()[['Retr.', 'TP', '?', 'FP', 'TN', 'FN', 'Prec.', 'Rec.', '$F_1$']].round(2).to_latex())\n"
     ]
    }
   ],
   "source": [
    "print(combinations_table.reset_index()[['Retr.', 'TP', '?', 'FP', 'TN', 'FN', 'Prec.', 'Rec.', '$F_1$']].round(2).to_latex())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Remaining False Positives"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['RCTs', 'Phase >= 2', 'Phase >= 3', 'Exclude Children', 'Significant', 'R', 'NR'])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics_combinations_final['Only RCTs'].keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "ids_false_positives = (\n",
    "    metrics_to_df(metrics_combinations_final[\"Only RCTs\"], drop_columns=[])\n",
    "    .sort_values([\"Recall\", \"Precision\", \"Retrieved\"], ascending=[False, False, True])\n",
    "    .iloc[0][\"Ids_fp\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "false_positives = df_s_with_ggponc[\n",
    "    df_s_with_ggponc[\"pm_id\"].apply(lambda pm_id: pm_id in ids_false_positives)\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>exclusion_reason</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TIAB1_exclude</th>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TIAB2_exclude</th>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FULLTEXT_exclude_Falsche Intervention_Comparison</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FULLTEXT_exclude_Abstract oder Protokoll</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FULLTEXT_exclude_Nicht die gesuchte Fragestellung</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FULLTEXT_exclude_Falsche Population</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gastric Cancer als Population nicht passend</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nicht Teil der PICO Fragen -  Ernährung als Intervention</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nicht Teil der PICO Fragen - Immuntherapie + Chemo, Toripalimab nicht explizit gesucht</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nicht Teil der PICO Fragen - keine Dosisvergleiche gesucht</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nicht Teil der PICO Fragen - keine Genexpressionsstudien gesucht</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>narrativer Review, ausgeschlossen</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    Count\n",
       "exclusion_reason                                         \n",
       "TIAB1_exclude                                          88\n",
       "TIAB2_exclude                                          34\n",
       "FULLTEXT_exclude_Falsche Intervention_Comparison        7\n",
       "FULLTEXT_exclude_Abstract oder Protokoll                6\n",
       "FULLTEXT_exclude_Nicht die gesuchte Fragestellung       4\n",
       "FULLTEXT_exclude_Falsche Population                     1\n",
       "Gastric Cancer als Population nicht passend             1\n",
       "Nicht Teil der PICO Fragen -  Ernährung als Int...      1\n",
       "Nicht Teil der PICO Fragen - Immuntherapie + Ch...      1\n",
       "Nicht Teil der PICO Fragen - keine Dosisverglei...      1\n",
       "Nicht Teil der PICO Fragen - keine Genexpressio...      1\n",
       "narrativer Review, ausgeschlossen                       1"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "false_positives.value_counts(\"exclusion_reason\").to_frame(name=\"Count\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>abstract</th>\n",
       "      <th>pm_id</th>\n",
       "      <th>nct_id</th>\n",
       "      <th>cn_id</th>\n",
       "      <th>doi</th>\n",
       "      <th>ris_id</th>\n",
       "      <th>publication_date</th>\n",
       "      <th>article_date_api</th>\n",
       "      <th>guideline_id</th>\n",
       "      <th>...</th>\n",
       "      <th>is_relevant</th>\n",
       "      <th>is_not_relevant</th>\n",
       "      <th>is_in_db</th>\n",
       "      <th>is_in_ggponc</th>\n",
       "      <th>screening_origin</th>\n",
       "      <th>rq</th>\n",
       "      <th>rqs_and_reasons</th>\n",
       "      <th>source</th>\n",
       "      <th>publication_date_combined</th>\n",
       "      <th>is_screened</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>Phase III study of tri-modality combination th...</td>\n",
       "      <td>A randomized phase III trial commenced in Japa...</td>\n",
       "      <td>31411696</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31411696</td>\n",
       "      <td>2019-08-14</td>\n",
       "      <td>NaT</td>\n",
       "      <td>oesophaguskarzinom</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>original</td>\n",
       "      <td>AG7 Multimodale Therapie - Stellenwert und Ind...</td>\n",
       "      <td>[{'rq': 'AG10 Multimodale Therapie - Stellenwe...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>286</th>\n",
       "      <td>Association between time interval from neoadju...</td>\n",
       "      <td>The optimal time interval from neoadjuvant the...</td>\n",
       "      <td>31676895</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31676895</td>\n",
       "      <td>2020-05-15</td>\n",
       "      <td>NaT</td>\n",
       "      <td>oesophaguskarzinom</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>original</td>\n",
       "      <td>AG10 Multimodale Therapie - Stellenwert der Ko...</td>\n",
       "      <td>[{'rq': 'AG3', 'exclusion_reason': 'TIAB1_excl...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>Preoperative chemotherapy compared with postop...</td>\n",
       "      <td>BACKGROUND: The role of preoperative chemother...</td>\n",
       "      <td>31756547</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31756547</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>2019-11-20</td>\n",
       "      <td>oesophaguskarzinom</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>original</td>\n",
       "      <td>AG9 Multimodale Therapie - Stellenwert der Ope...</td>\n",
       "      <td>[{'rq': 'AG10 Multimodale Therapie - Stellenwe...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479</th>\n",
       "      <td>A phase-II/III randomized controlled trial of ...</td>\n",
       "      <td>BACKGROUND: Preoperative chemoradiotherapy (CR...</td>\n",
       "      <td>32070309</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32070309</td>\n",
       "      <td>2020-02-18</td>\n",
       "      <td>2020-02-18</td>\n",
       "      <td>oesophaguskarzinom</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>original</td>\n",
       "      <td>AG9 Multimodale Therapie - Stellenwert der Ope...</td>\n",
       "      <td>[{'rq': 'AG2 Endoskopische Therapie - Vorgehen...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>High thromboembolic event rate in patients wit...</td>\n",
       "      <td>BACKGROUND: High rates of venous thromboemboli...</td>\n",
       "      <td>32111181</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32111181</td>\n",
       "      <td>2020-02-28</td>\n",
       "      <td>2020-02-28</td>\n",
       "      <td>oesophaguskarzinom</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>original</td>\n",
       "      <td>AG9 Multimodale Therapie - Stellenwert der Ope...</td>\n",
       "      <td>[{'rq': 'AG10 Multimodale Therapie - Stellenwe...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>705</th>\n",
       "      <td>Radiochemotherapy with or without cetuximab fo...</td>\n",
       "      <td>PURPOSE: To investigate the efficacy and toxic...</td>\n",
       "      <td>32533228</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32533228</td>\n",
       "      <td>2020-09-01</td>\n",
       "      <td>2020-06-12</td>\n",
       "      <td>oesophaguskarzinom</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>original</td>\n",
       "      <td>AG7 Multimodale Therapie - Stellenwert und Ind...</td>\n",
       "      <td>[{'rq': 'AG5 Chirurgische Therapie - Stellenwe...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>768</th>\n",
       "      <td>Endoscopic submucosal dissection with addition...</td>\n",
       "      <td>BACKGROUND: Endoscopic submucosal dissection (...</td>\n",
       "      <td>32668474</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32668474</td>\n",
       "      <td>2020-12-01</td>\n",
       "      <td>2020-07-15</td>\n",
       "      <td>oesophaguskarzinom</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>original</td>\n",
       "      <td>AG10 Multimodale Therapie - Stellenwert der Ko...</td>\n",
       "      <td>[{'rq': 'AG1 Endoskopische Therapie - Indikati...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868</th>\n",
       "      <td>Recurrence patterns after neoadjuvant chemorad...</td>\n",
       "      <td>BACKGROUND: The aim of this study was to compa...</td>\n",
       "      <td>32877795</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32877795</td>\n",
       "      <td>2020-10-01</td>\n",
       "      <td>2020-08-30</td>\n",
       "      <td>oesophaguskarzinom</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>original</td>\n",
       "      <td>AG10 Multimodale Therapie - Stellenwert der Ko...</td>\n",
       "      <td>[{'rq': 'AG2 Endoskopische Therapie - Vorgehen...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>898</th>\n",
       "      <td>RACE-trial: neoadjuvant radiochemotherapy vers...</td>\n",
       "      <td>BACKGROUND: Despite obvious advances over the ...</td>\n",
       "      <td>32933498</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32933498</td>\n",
       "      <td>2020-09-15</td>\n",
       "      <td>2020-09-15</td>\n",
       "      <td>oesophaguskarzinom</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>original</td>\n",
       "      <td>AG9 Multimodale Therapie - Stellenwert der Ope...</td>\n",
       "      <td>[{'rq': 'AG10 Multimodale Therapie - Stellenwe...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>933</th>\n",
       "      <td>Comparison of transhiatal esophagectomy using ...</td>\n",
       "      <td>BACKGROUND: This study aimed to evaluate the r...</td>\n",
       "      <td>32998523</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32998523</td>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>2020-09-30</td>\n",
       "      <td>oesophaguskarzinom</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>original</td>\n",
       "      <td>AG3</td>\n",
       "      <td>[{'rq': 'AG8  Multimodale Therapie - Rolle des...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>964</th>\n",
       "      <td>Multicenter Randomized Phase 2 Trial Comparing...</td>\n",
       "      <td>OBJECTIVE: We conducted a multicenter randomiz...</td>\n",
       "      <td>33065643</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33065643</td>\n",
       "      <td>2021-12-01</td>\n",
       "      <td>NaT</td>\n",
       "      <td>oesophaguskarzinom</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>original</td>\n",
       "      <td>AG7 Multimodale Therapie - Stellenwert und Ind...</td>\n",
       "      <td>[{'rq': 'AG10 Multimodale Therapie - Stellenwe...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1265</th>\n",
       "      <td>Involved-Field Irradiation in Definitive Chemo...</td>\n",
       "      <td>PURPOSE: To evaluate the feasibility and effic...</td>\n",
       "      <td>33677048</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33677048</td>\n",
       "      <td>2021-08-01</td>\n",
       "      <td>2021-03-05</td>\n",
       "      <td>oesophaguskarzinom</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>original</td>\n",
       "      <td>AG7 Multimodale Therapie - Stellenwert und Ind...</td>\n",
       "      <td>[{'rq': 'AG4 Chirurgische Therapie - Stellenwe...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1489</th>\n",
       "      <td>The efficacy of adjuvant chemotherapy with cap...</td>\n",
       "      <td>There is limited evidence for the effectivenes...</td>\n",
       "      <td>34155501</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34155501</td>\n",
       "      <td>2022-01-07</td>\n",
       "      <td>NaT</td>\n",
       "      <td>oesophaguskarzinom</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>original</td>\n",
       "      <td>AG6 Multimodale Therapie - Verbessert eine adj...</td>\n",
       "      <td>[{'rq': 'AG2 Endoskopische Therapie - Vorgehen...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1510</th>\n",
       "      <td>Induction chemotherapy followed by definitive ...</td>\n",
       "      <td>This randomized phase II trial aims to compare...</td>\n",
       "      <td>34188053</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34188053</td>\n",
       "      <td>2021-06-29</td>\n",
       "      <td>2021-06-29</td>\n",
       "      <td>oesophaguskarzinom</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>original</td>\n",
       "      <td>AG7 Multimodale Therapie - Stellenwert und Ind...</td>\n",
       "      <td>[{'rq': 'AG10 Multimodale Therapie - Stellenwe...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1537</th>\n",
       "      <td>Perioperative or postoperative adjuvant oxalip...</td>\n",
       "      <td>BACKGROUND: The optimal perioperative chemothe...</td>\n",
       "      <td>34252374</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34252374</td>\n",
       "      <td>2021-08-01</td>\n",
       "      <td>2021-07-09</td>\n",
       "      <td>oesophaguskarzinom</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>original</td>\n",
       "      <td>AG11 Palliative Therapie - Stellenwert der pal...</td>\n",
       "      <td>[{'rq': 'AG6 Multimodale Therapie - Verbessert...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1540</th>\n",
       "      <td>RATIONALE 311: tislelizumab plus concurrent ch...</td>\n",
       "      <td>Definitive chemoradiotherapy is the standard o...</td>\n",
       "      <td>34269067</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34269067</td>\n",
       "      <td>2021-11-01</td>\n",
       "      <td>2021-07-16</td>\n",
       "      <td>oesophaguskarzinom</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>original</td>\n",
       "      <td>AG7 Multimodale Therapie - Stellenwert und Ind...</td>\n",
       "      <td>[{'rq': 'AG5 Chirurgische Therapie - Stellenwe...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1560</th>\n",
       "      <td>Randomized phase II study of docetaxel versus ...</td>\n",
       "      <td>BACKGROUND: There is no standard chemotherapy ...</td>\n",
       "      <td>34311300</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34311300</td>\n",
       "      <td>2021-09-01</td>\n",
       "      <td>2021-07-24</td>\n",
       "      <td>oesophaguskarzinom</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>original</td>\n",
       "      <td>AG12 Palliative Therapie - Stellenwert der Zwe...</td>\n",
       "      <td>[{'rq': 'AG12 Palliative Therapie - Stellenwer...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1641</th>\n",
       "      <td>Exploration of predictors of benefit from nivo...</td>\n",
       "      <td>BACKGROUND: The phase 3 ATTRACTION-2 study dem...</td>\n",
       "      <td>34480657</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34480657</td>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>2021-09-04</td>\n",
       "      <td>oesophaguskarzinom</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>original</td>\n",
       "      <td>AG9 Multimodale Therapie - Stellenwert der Ope...</td>\n",
       "      <td>[{'rq': 'AG9 Multimodale Therapie - Stellenwer...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1657</th>\n",
       "      <td>Apatinib Combined with Paclitaxel and Cisplati...</td>\n",
       "      <td>Background: We conducted a trial to evaluate t...</td>\n",
       "      <td>34524004</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34524004</td>\n",
       "      <td>2021-09-15</td>\n",
       "      <td>2021-09-15</td>\n",
       "      <td>oesophaguskarzinom</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>original</td>\n",
       "      <td>AG10 Multimodale Therapie - Stellenwert der Ko...</td>\n",
       "      <td>[{'rq': 'AG3', 'exclusion_reason': 'TIAB1_excl...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1719</th>\n",
       "      <td>Video-assisted mediastinoscopic and laparoscop...</td>\n",
       "      <td>BACKGROUND: Mediastinoscopy was originally app...</td>\n",
       "      <td>34642798</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34642798</td>\n",
       "      <td>2021-10-12</td>\n",
       "      <td>2021-10-12</td>\n",
       "      <td>oesophaguskarzinom</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>original</td>\n",
       "      <td>AG3</td>\n",
       "      <td>[{'rq': 'AG2 Endoskopische Therapie - Vorgehen...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2222</th>\n",
       "      <td>Efficacy of trastuzumab emtansine in Japanese ...</td>\n",
       "      <td>AIM: The phase II/III GATSBY study (NCT0164193...</td>\n",
       "      <td>31721447</td>\n",
       "      <td>NCT01641939</td>\n",
       "      <td>CN-02008823</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CN-02008823</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>2019-11-12</td>\n",
       "      <td>oesophaguskarzinom</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>original</td>\n",
       "      <td>AG11 Palliative Therapie - Stellenwert der pal...</td>\n",
       "      <td>[{'rq': 'AG11 Palliative Therapie - Stellenwer...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2272</th>\n",
       "      <td>Prognostic Impact of Postoperative Lymph Node ...</td>\n",
       "      <td>OBJECTIVE: To determine the prognostic impact ...</td>\n",
       "      <td>31855875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CN-02073832</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CN-02073832</td>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>NaT</td>\n",
       "      <td>oesophaguskarzinom</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>original</td>\n",
       "      <td>AG4 Chirurgische Therapie - Stellenwert der st...</td>\n",
       "      <td>[{'rq': 'AG9 Multimodale Therapie - Stellenwer...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2410</th>\n",
       "      <td>Biomarker analyses of second-line ramucirumab ...</td>\n",
       "      <td>Background: The RAINBOW trial showed that seco...</td>\n",
       "      <td>32014812</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CN-02136892</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CN-02136892</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>2020-01-31</td>\n",
       "      <td>oesophaguskarzinom</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>original</td>\n",
       "      <td>AG12 Palliative Therapie - Stellenwert der Zwe...</td>\n",
       "      <td>[{'rq': 'AG12 Palliative Therapie - Stellenwer...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2439</th>\n",
       "      <td>A multicenter randomized prospective study of ...</td>\n",
       "      <td>Objective: To determine whether 60 Gy is super...</td>\n",
       "      <td>32536123</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CN-02144435</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CN-02144435</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>NaT</td>\n",
       "      <td>oesophaguskarzinom</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>original</td>\n",
       "      <td>AG7 Multimodale Therapie - Stellenwert und Ind...</td>\n",
       "      <td>[{'rq': 'AG4 Chirurgische Therapie - Stellenwe...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2635</th>\n",
       "      <td>Nivolumab versus chemotherapy in Japanese pati...</td>\n",
       "      <td>Background: The efficacy and safety of nivolum...</td>\n",
       "      <td>33170461</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CN-02204806</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CN-02204806</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>2020-11-10</td>\n",
       "      <td>oesophaguskarzinom</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>original</td>\n",
       "      <td>AG12 Palliative Therapie - Stellenwert der Zwe...</td>\n",
       "      <td>[{'rq': 'AG12 Palliative Therapie - Stellenwer...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2637</th>\n",
       "      <td>EGFR amplification and outcome in a randomised...</td>\n",
       "      <td>Objective Epidermal growth factor receptor (EG...</td>\n",
       "      <td>33199443</td>\n",
       "      <td>NCT00824785</td>\n",
       "      <td>CN-02205519</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CN-02205519</td>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>2020-11-16</td>\n",
       "      <td>oesophaguskarzinom</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>original</td>\n",
       "      <td>AG11 Palliative Therapie - Stellenwert der pal...</td>\n",
       "      <td>[{'rq': 'AG11 Palliative Therapie - Stellenwer...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2663</th>\n",
       "      <td>Adjuvant statin therapy for oesophageal adenoc...</td>\n",
       "      <td>BACKGROUND: Statins inhibit proliferative sign...</td>\n",
       "      <td>32011825</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CN-02233339</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CN-02233339</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>2019-12-14</td>\n",
       "      <td>oesophaguskarzinom</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>original</td>\n",
       "      <td>AG6 Multimodale Therapie - Verbessert eine adj...</td>\n",
       "      <td>[{'rq': 'AG6 Multimodale Therapie - Verbessert...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2712</th>\n",
       "      <td>Randomized Phase II Study to Comparing Docetax...</td>\n",
       "      <td>PURPOSE: To compare efficacy and safety of dua...</td>\n",
       "      <td>33408308</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CN-02248351</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CN-02248351</td>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>2021-01-06</td>\n",
       "      <td>oesophaguskarzinom</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>original</td>\n",
       "      <td>AG12 Palliative Therapie - Stellenwert der Zwe...</td>\n",
       "      <td>[{'rq': 'AG11 Palliative Therapie - Stellenwer...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2730</th>\n",
       "      <td>Five-Year Survival Outcomes of Hybrid Minimall...</td>\n",
       "      <td>Importance: Available data comparing the long?...</td>\n",
       "      <td>33595631</td>\n",
       "      <td>NCT00937456</td>\n",
       "      <td>CN-02258543</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CN-02258543</td>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>NaT</td>\n",
       "      <td>oesophaguskarzinom</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>original</td>\n",
       "      <td>AG2 Endoskopische Therapie - Vorgehen bei Loka...</td>\n",
       "      <td>[{'rq': 'AG2 Endoskopische Therapie - Vorgehen...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2808</th>\n",
       "      <td>Induction versus no induction chemotherapy bef...</td>\n",
       "      <td>Aim: report primary results from the first mul...</td>\n",
       "      <td>33934058</td>\n",
       "      <td>NCT00938470</td>\n",
       "      <td>CN-02284627</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CN-02284627</td>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>2021-04-29</td>\n",
       "      <td>oesophaguskarzinom</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>original</td>\n",
       "      <td>AG9 Multimodale Therapie - Stellenwert der Ope...</td>\n",
       "      <td>[{'rq': 'AG6 Multimodale Therapie - Verbessert...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2941</th>\n",
       "      <td>Second-line pembrolizumab versus chemotherapy ...</td>\n",
       "      <td>Background: Safe and effective treatments for ...</td>\n",
       "      <td>34591237</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CN-02338198</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CN-02338198</td>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>2021-09-30</td>\n",
       "      <td>oesophaguskarzinom</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>original</td>\n",
       "      <td>AG12 Palliative Therapie - Stellenwert der Zwe...</td>\n",
       "      <td>[{'rq': 'AG11 Palliative Therapie - Stellenwer...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2949</th>\n",
       "      <td>Efficacy and safety of weekly paclitaxel with ...</td>\n",
       "      <td>Background: In the global phase 3 RAINBOW stud...</td>\n",
       "      <td>34626550</td>\n",
       "      <td>NCT02898077</td>\n",
       "      <td>CN-02342195</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CN-02342195</td>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>2021-10-07</td>\n",
       "      <td>oesophaguskarzinom</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>original</td>\n",
       "      <td>AG12 Palliative Therapie - Stellenwert der Zwe...</td>\n",
       "      <td>[{'rq': 'AG12 Palliative Therapie - Stellenwer...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2973</th>\n",
       "      <td>Pembrolizumab versus paclitaxel for previously...</td>\n",
       "      <td>Background: KEYNOTE?063 (NCT03019588) investig...</td>\n",
       "      <td>34878659</td>\n",
       "      <td>NCT03019588</td>\n",
       "      <td>CN-02348570</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CN-02348570</td>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>2021-12-08</td>\n",
       "      <td>oesophaguskarzinom</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>original</td>\n",
       "      <td>AG12 Palliative Therapie - Stellenwert der Zwe...</td>\n",
       "      <td>[{'rq': 'AG4 Chirurgische Therapie - Stellenwe...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3004</th>\n",
       "      <td>PRODIGE 59-DURIGAST trial: a randomised phase ...</td>\n",
       "      <td>Gastric or gastro?oesophageal junction (GEJ) a...</td>\n",
       "      <td>33358124</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CN-02366298</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CN-02366298</td>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>2021-01-06</td>\n",
       "      <td>oesophaguskarzinom</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>original</td>\n",
       "      <td>AG12 Palliative Therapie - Stellenwert der Zwe...</td>\n",
       "      <td>[{'rq': 'AG11 Palliative Therapie - Stellenwer...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>34 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  title  \\\n",
       "154   Phase III study of tri-modality combination th...   \n",
       "286   Association between time interval from neoadju...   \n",
       "318   Preoperative chemotherapy compared with postop...   \n",
       "479   A phase-II/III randomized controlled trial of ...   \n",
       "502   High thromboembolic event rate in patients wit...   \n",
       "705   Radiochemotherapy with or without cetuximab fo...   \n",
       "768   Endoscopic submucosal dissection with addition...   \n",
       "868   Recurrence patterns after neoadjuvant chemorad...   \n",
       "898   RACE-trial: neoadjuvant radiochemotherapy vers...   \n",
       "933   Comparison of transhiatal esophagectomy using ...   \n",
       "964   Multicenter Randomized Phase 2 Trial Comparing...   \n",
       "1265  Involved-Field Irradiation in Definitive Chemo...   \n",
       "1489  The efficacy of adjuvant chemotherapy with cap...   \n",
       "1510  Induction chemotherapy followed by definitive ...   \n",
       "1537  Perioperative or postoperative adjuvant oxalip...   \n",
       "1540  RATIONALE 311: tislelizumab plus concurrent ch...   \n",
       "1560  Randomized phase II study of docetaxel versus ...   \n",
       "1641  Exploration of predictors of benefit from nivo...   \n",
       "1657  Apatinib Combined with Paclitaxel and Cisplati...   \n",
       "1719  Video-assisted mediastinoscopic and laparoscop...   \n",
       "2222  Efficacy of trastuzumab emtansine in Japanese ...   \n",
       "2272  Prognostic Impact of Postoperative Lymph Node ...   \n",
       "2410  Biomarker analyses of second-line ramucirumab ...   \n",
       "2439  A multicenter randomized prospective study of ...   \n",
       "2635  Nivolumab versus chemotherapy in Japanese pati...   \n",
       "2637  EGFR amplification and outcome in a randomised...   \n",
       "2663  Adjuvant statin therapy for oesophageal adenoc...   \n",
       "2712  Randomized Phase II Study to Comparing Docetax...   \n",
       "2730  Five-Year Survival Outcomes of Hybrid Minimall...   \n",
       "2808  Induction versus no induction chemotherapy bef...   \n",
       "2941  Second-line pembrolizumab versus chemotherapy ...   \n",
       "2949  Efficacy and safety of weekly paclitaxel with ...   \n",
       "2973  Pembrolizumab versus paclitaxel for previously...   \n",
       "3004  PRODIGE 59-DURIGAST trial: a randomised phase ...   \n",
       "\n",
       "                                               abstract     pm_id  \\\n",
       "154   A randomized phase III trial commenced in Japa...  31411696   \n",
       "286   The optimal time interval from neoadjuvant the...  31676895   \n",
       "318   BACKGROUND: The role of preoperative chemother...  31756547   \n",
       "479   BACKGROUND: Preoperative chemoradiotherapy (CR...  32070309   \n",
       "502   BACKGROUND: High rates of venous thromboemboli...  32111181   \n",
       "705   PURPOSE: To investigate the efficacy and toxic...  32533228   \n",
       "768   BACKGROUND: Endoscopic submucosal dissection (...  32668474   \n",
       "868   BACKGROUND: The aim of this study was to compa...  32877795   \n",
       "898   BACKGROUND: Despite obvious advances over the ...  32933498   \n",
       "933   BACKGROUND: This study aimed to evaluate the r...  32998523   \n",
       "964   OBJECTIVE: We conducted a multicenter randomiz...  33065643   \n",
       "1265  PURPOSE: To evaluate the feasibility and effic...  33677048   \n",
       "1489  There is limited evidence for the effectivenes...  34155501   \n",
       "1510  This randomized phase II trial aims to compare...  34188053   \n",
       "1537  BACKGROUND: The optimal perioperative chemothe...  34252374   \n",
       "1540  Definitive chemoradiotherapy is the standard o...  34269067   \n",
       "1560  BACKGROUND: There is no standard chemotherapy ...  34311300   \n",
       "1641  BACKGROUND: The phase 3 ATTRACTION-2 study dem...  34480657   \n",
       "1657  Background: We conducted a trial to evaluate t...  34524004   \n",
       "1719  BACKGROUND: Mediastinoscopy was originally app...  34642798   \n",
       "2222  AIM: The phase II/III GATSBY study (NCT0164193...  31721447   \n",
       "2272  OBJECTIVE: To determine the prognostic impact ...  31855875   \n",
       "2410  Background: The RAINBOW trial showed that seco...  32014812   \n",
       "2439  Objective: To determine whether 60 Gy is super...  32536123   \n",
       "2635  Background: The efficacy and safety of nivolum...  33170461   \n",
       "2637  Objective Epidermal growth factor receptor (EG...  33199443   \n",
       "2663  BACKGROUND: Statins inhibit proliferative sign...  32011825   \n",
       "2712  PURPOSE: To compare efficacy and safety of dua...  33408308   \n",
       "2730  Importance: Available data comparing the long?...  33595631   \n",
       "2808  Aim: report primary results from the first mul...  33934058   \n",
       "2941  Background: Safe and effective treatments for ...  34591237   \n",
       "2949  Background: In the global phase 3 RAINBOW stud...  34626550   \n",
       "2973  Background: KEYNOTE?063 (NCT03019588) investig...  34878659   \n",
       "3004  Gastric or gastro?oesophageal junction (GEJ) a...  33358124   \n",
       "\n",
       "           nct_id        cn_id  doi       ris_id publication_date  \\\n",
       "154           NaN          NaN  NaN     31411696       2019-08-14   \n",
       "286           NaN          NaN  NaN     31676895       2020-05-15   \n",
       "318           NaN          NaN  NaN     31756547       2020-01-01   \n",
       "479           NaN          NaN  NaN     32070309       2020-02-18   \n",
       "502           NaN          NaN  NaN     32111181       2020-02-28   \n",
       "705           NaN          NaN  NaN     32533228       2020-09-01   \n",
       "768           NaN          NaN  NaN     32668474       2020-12-01   \n",
       "868           NaN          NaN  NaN     32877795       2020-10-01   \n",
       "898           NaN          NaN  NaN     32933498       2020-09-15   \n",
       "933           NaN          NaN  NaN     32998523       2021-01-01   \n",
       "964           NaN          NaN  NaN     33065643       2021-12-01   \n",
       "1265          NaN          NaN  NaN     33677048       2021-08-01   \n",
       "1489          NaN          NaN  NaN     34155501       2022-01-07   \n",
       "1510          NaN          NaN  NaN     34188053       2021-06-29   \n",
       "1537          NaN          NaN  NaN     34252374       2021-08-01   \n",
       "1540          NaN          NaN  NaN     34269067       2021-11-01   \n",
       "1560          NaN          NaN  NaN     34311300       2021-09-01   \n",
       "1641          NaN          NaN  NaN     34480657       2022-01-01   \n",
       "1657          NaN          NaN  NaN     34524004       2021-09-15   \n",
       "1719          NaN          NaN  NaN     34642798       2021-10-12   \n",
       "2222  NCT01641939  CN-02008823  NaN  CN-02008823       2020-01-01   \n",
       "2272          NaN  CN-02073832  NaN  CN-02073832       2021-01-01   \n",
       "2410          NaN  CN-02136892  NaN  CN-02136892       2020-01-01   \n",
       "2439          NaN  CN-02144435  NaN  CN-02144435       2020-01-01   \n",
       "2635          NaN  CN-02204806  NaN  CN-02204806       2020-01-01   \n",
       "2637  NCT00824785  CN-02205519  NaN  CN-02205519       2021-01-01   \n",
       "2663          NaN  CN-02233339  NaN  CN-02233339       2020-01-01   \n",
       "2712          NaN  CN-02248351  NaN  CN-02248351       2021-01-01   \n",
       "2730  NCT00937456  CN-02258543  NaN  CN-02258543       2021-01-01   \n",
       "2808  NCT00938470  CN-02284627  NaN  CN-02284627       2021-01-01   \n",
       "2941          NaN  CN-02338198  NaN  CN-02338198       2021-01-01   \n",
       "2949  NCT02898077  CN-02342195  NaN  CN-02342195       2021-01-01   \n",
       "2973  NCT03019588  CN-02348570  NaN  CN-02348570       2021-01-01   \n",
       "3004          NaN  CN-02366298  NaN  CN-02366298       2021-01-01   \n",
       "\n",
       "     article_date_api        guideline_id  ... is_relevant is_not_relevant  \\\n",
       "154               NaT  oesophaguskarzinom  ...       False            True   \n",
       "286               NaT  oesophaguskarzinom  ...       False            True   \n",
       "318        2019-11-20  oesophaguskarzinom  ...       False            True   \n",
       "479        2020-02-18  oesophaguskarzinom  ...       False            True   \n",
       "502        2020-02-28  oesophaguskarzinom  ...       False            True   \n",
       "705        2020-06-12  oesophaguskarzinom  ...       False            True   \n",
       "768        2020-07-15  oesophaguskarzinom  ...       False            True   \n",
       "868        2020-08-30  oesophaguskarzinom  ...       False            True   \n",
       "898        2020-09-15  oesophaguskarzinom  ...       False            True   \n",
       "933        2020-09-30  oesophaguskarzinom  ...       False            True   \n",
       "964               NaT  oesophaguskarzinom  ...       False            True   \n",
       "1265       2021-03-05  oesophaguskarzinom  ...       False            True   \n",
       "1489              NaT  oesophaguskarzinom  ...       False            True   \n",
       "1510       2021-06-29  oesophaguskarzinom  ...       False            True   \n",
       "1537       2021-07-09  oesophaguskarzinom  ...       False            True   \n",
       "1540       2021-07-16  oesophaguskarzinom  ...       False            True   \n",
       "1560       2021-07-24  oesophaguskarzinom  ...       False            True   \n",
       "1641       2021-09-04  oesophaguskarzinom  ...       False            True   \n",
       "1657       2021-09-15  oesophaguskarzinom  ...       False            True   \n",
       "1719       2021-10-12  oesophaguskarzinom  ...       False            True   \n",
       "2222       2019-11-12  oesophaguskarzinom  ...       False            True   \n",
       "2272              NaT  oesophaguskarzinom  ...       False            True   \n",
       "2410       2020-01-31  oesophaguskarzinom  ...       False            True   \n",
       "2439              NaT  oesophaguskarzinom  ...       False            True   \n",
       "2635       2020-11-10  oesophaguskarzinom  ...       False            True   \n",
       "2637       2020-11-16  oesophaguskarzinom  ...       False            True   \n",
       "2663       2019-12-14  oesophaguskarzinom  ...       False            True   \n",
       "2712       2021-01-06  oesophaguskarzinom  ...       False            True   \n",
       "2730              NaT  oesophaguskarzinom  ...       False            True   \n",
       "2808       2021-04-29  oesophaguskarzinom  ...       False            True   \n",
       "2941       2021-09-30  oesophaguskarzinom  ...       False            True   \n",
       "2949       2021-10-07  oesophaguskarzinom  ...       False            True   \n",
       "2973       2021-12-08  oesophaguskarzinom  ...       False            True   \n",
       "3004       2021-01-06  oesophaguskarzinom  ...       False            True   \n",
       "\n",
       "     is_in_db  is_in_ggponc screening_origin  \\\n",
       "154      True         False         original   \n",
       "286      True         False         original   \n",
       "318      True         False         original   \n",
       "479      True         False         original   \n",
       "502      True         False         original   \n",
       "705      True         False         original   \n",
       "768      True         False         original   \n",
       "868      True         False         original   \n",
       "898      True         False         original   \n",
       "933      True         False         original   \n",
       "964      True         False         original   \n",
       "1265     True         False         original   \n",
       "1489     True         False         original   \n",
       "1510     True         False         original   \n",
       "1537     True         False         original   \n",
       "1540     True         False         original   \n",
       "1560     True         False         original   \n",
       "1641     True         False         original   \n",
       "1657     True         False         original   \n",
       "1719     True         False         original   \n",
       "2222     True         False         original   \n",
       "2272     True         False         original   \n",
       "2410     True         False         original   \n",
       "2439     True         False         original   \n",
       "2635     True         False         original   \n",
       "2637     True         False         original   \n",
       "2663     True         False         original   \n",
       "2712     True         False         original   \n",
       "2730     True         False         original   \n",
       "2808     True         False         original   \n",
       "2941     True         False         original   \n",
       "2949     True         False         original   \n",
       "2973     True         False         original   \n",
       "3004     True         False         original   \n",
       "\n",
       "                                                     rq  \\\n",
       "154   AG7 Multimodale Therapie - Stellenwert und Ind...   \n",
       "286   AG10 Multimodale Therapie - Stellenwert der Ko...   \n",
       "318   AG9 Multimodale Therapie - Stellenwert der Ope...   \n",
       "479   AG9 Multimodale Therapie - Stellenwert der Ope...   \n",
       "502   AG9 Multimodale Therapie - Stellenwert der Ope...   \n",
       "705   AG7 Multimodale Therapie - Stellenwert und Ind...   \n",
       "768   AG10 Multimodale Therapie - Stellenwert der Ko...   \n",
       "868   AG10 Multimodale Therapie - Stellenwert der Ko...   \n",
       "898   AG9 Multimodale Therapie - Stellenwert der Ope...   \n",
       "933                                                 AG3   \n",
       "964   AG7 Multimodale Therapie - Stellenwert und Ind...   \n",
       "1265  AG7 Multimodale Therapie - Stellenwert und Ind...   \n",
       "1489  AG6 Multimodale Therapie - Verbessert eine adj...   \n",
       "1510  AG7 Multimodale Therapie - Stellenwert und Ind...   \n",
       "1537  AG11 Palliative Therapie - Stellenwert der pal...   \n",
       "1540  AG7 Multimodale Therapie - Stellenwert und Ind...   \n",
       "1560  AG12 Palliative Therapie - Stellenwert der Zwe...   \n",
       "1641  AG9 Multimodale Therapie - Stellenwert der Ope...   \n",
       "1657  AG10 Multimodale Therapie - Stellenwert der Ko...   \n",
       "1719                                                AG3   \n",
       "2222  AG11 Palliative Therapie - Stellenwert der pal...   \n",
       "2272  AG4 Chirurgische Therapie - Stellenwert der st...   \n",
       "2410  AG12 Palliative Therapie - Stellenwert der Zwe...   \n",
       "2439  AG7 Multimodale Therapie - Stellenwert und Ind...   \n",
       "2635  AG12 Palliative Therapie - Stellenwert der Zwe...   \n",
       "2637  AG11 Palliative Therapie - Stellenwert der pal...   \n",
       "2663  AG6 Multimodale Therapie - Verbessert eine adj...   \n",
       "2712  AG12 Palliative Therapie - Stellenwert der Zwe...   \n",
       "2730  AG2 Endoskopische Therapie - Vorgehen bei Loka...   \n",
       "2808  AG9 Multimodale Therapie - Stellenwert der Ope...   \n",
       "2941  AG12 Palliative Therapie - Stellenwert der Zwe...   \n",
       "2949  AG12 Palliative Therapie - Stellenwert der Zwe...   \n",
       "2973  AG12 Palliative Therapie - Stellenwert der Zwe...   \n",
       "3004  AG12 Palliative Therapie - Stellenwert der Zwe...   \n",
       "\n",
       "                                        rqs_and_reasons  source  \\\n",
       "154   [{'rq': 'AG10 Multimodale Therapie - Stellenwe...     NaN   \n",
       "286   [{'rq': 'AG3', 'exclusion_reason': 'TIAB1_excl...     NaN   \n",
       "318   [{'rq': 'AG10 Multimodale Therapie - Stellenwe...     NaN   \n",
       "479   [{'rq': 'AG2 Endoskopische Therapie - Vorgehen...     NaN   \n",
       "502   [{'rq': 'AG10 Multimodale Therapie - Stellenwe...     NaN   \n",
       "705   [{'rq': 'AG5 Chirurgische Therapie - Stellenwe...     NaN   \n",
       "768   [{'rq': 'AG1 Endoskopische Therapie - Indikati...     NaN   \n",
       "868   [{'rq': 'AG2 Endoskopische Therapie - Vorgehen...     NaN   \n",
       "898   [{'rq': 'AG10 Multimodale Therapie - Stellenwe...     NaN   \n",
       "933   [{'rq': 'AG8  Multimodale Therapie - Rolle des...     NaN   \n",
       "964   [{'rq': 'AG10 Multimodale Therapie - Stellenwe...     NaN   \n",
       "1265  [{'rq': 'AG4 Chirurgische Therapie - Stellenwe...     NaN   \n",
       "1489  [{'rq': 'AG2 Endoskopische Therapie - Vorgehen...     NaN   \n",
       "1510  [{'rq': 'AG10 Multimodale Therapie - Stellenwe...     NaN   \n",
       "1537  [{'rq': 'AG6 Multimodale Therapie - Verbessert...     NaN   \n",
       "1540  [{'rq': 'AG5 Chirurgische Therapie - Stellenwe...     NaN   \n",
       "1560  [{'rq': 'AG12 Palliative Therapie - Stellenwer...     NaN   \n",
       "1641  [{'rq': 'AG9 Multimodale Therapie - Stellenwer...     NaN   \n",
       "1657  [{'rq': 'AG3', 'exclusion_reason': 'TIAB1_excl...     NaN   \n",
       "1719  [{'rq': 'AG2 Endoskopische Therapie - Vorgehen...     NaN   \n",
       "2222  [{'rq': 'AG11 Palliative Therapie - Stellenwer...     NaN   \n",
       "2272  [{'rq': 'AG9 Multimodale Therapie - Stellenwer...     NaN   \n",
       "2410  [{'rq': 'AG12 Palliative Therapie - Stellenwer...     NaN   \n",
       "2439  [{'rq': 'AG4 Chirurgische Therapie - Stellenwe...     NaN   \n",
       "2635  [{'rq': 'AG12 Palliative Therapie - Stellenwer...     NaN   \n",
       "2637  [{'rq': 'AG11 Palliative Therapie - Stellenwer...     NaN   \n",
       "2663  [{'rq': 'AG6 Multimodale Therapie - Verbessert...     NaN   \n",
       "2712  [{'rq': 'AG11 Palliative Therapie - Stellenwer...     NaN   \n",
       "2730  [{'rq': 'AG2 Endoskopische Therapie - Vorgehen...     NaN   \n",
       "2808  [{'rq': 'AG6 Multimodale Therapie - Verbessert...     NaN   \n",
       "2941  [{'rq': 'AG11 Palliative Therapie - Stellenwer...     NaN   \n",
       "2949  [{'rq': 'AG12 Palliative Therapie - Stellenwer...     NaN   \n",
       "2973  [{'rq': 'AG4 Chirurgische Therapie - Stellenwe...     NaN   \n",
       "3004  [{'rq': 'AG11 Palliative Therapie - Stellenwer...     NaN   \n",
       "\n",
       "      publication_date_combined  is_screened  \n",
       "154                         NaT         True  \n",
       "286                         NaT         True  \n",
       "318                         NaT         True  \n",
       "479                         NaT         True  \n",
       "502                         NaT         True  \n",
       "705                         NaT         True  \n",
       "768                         NaT         True  \n",
       "868                         NaT         True  \n",
       "898                         NaT         True  \n",
       "933                         NaT         True  \n",
       "964                         NaT         True  \n",
       "1265                        NaT         True  \n",
       "1489                        NaT         True  \n",
       "1510                        NaT         True  \n",
       "1537                        NaT         True  \n",
       "1540                        NaT         True  \n",
       "1560                        NaT         True  \n",
       "1641                        NaT         True  \n",
       "1657                        NaT         True  \n",
       "1719                        NaT         True  \n",
       "2222                        NaT         True  \n",
       "2272                        NaT         True  \n",
       "2410                        NaT         True  \n",
       "2439                        NaT         True  \n",
       "2635                        NaT         True  \n",
       "2637                        NaT         True  \n",
       "2663                        NaT         True  \n",
       "2712                        NaT         True  \n",
       "2730                        NaT         True  \n",
       "2808                        NaT         True  \n",
       "2941                        NaT         True  \n",
       "2949                        NaT         True  \n",
       "2973                        NaT         True  \n",
       "3004                        NaT         True  \n",
       "\n",
       "[34 rows x 26 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "false_positives[false_positives.exclusion_reason == 'TIAB2_exclude']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Missing RCTs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We should be able to find all included RCTs. If not, we need to find out why we did not retrieve them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{30982686, 31595970, 32421440, 32880601}"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics_w_manual[\"Only RCTs\"][\"All\"][\"ids_fn\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from api.utils import query_api_for_concepts, concepts_to_df\n",
    "from api.models import ConceptsQuery\n",
    "\n",
    "missing_rcts_df = concepts_to_df(\n",
    "    query_api_for_concepts(\n",
    "        ConceptsQuery(\n",
    "            source=\"pubmed\",\n",
    "            ids=[\n",
    "                int(pm_id) for pm_id in metrics_w_manual[\"Only RCTs\"][\"All\"][\"ids_fn\"]\n",
    "            ],\n",
    "            kind=\"population\",\n",
    "        )\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks like it is due to vague population concepts:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cui</th>\n",
       "      <th>term</th>\n",
       "      <th>text_umls</th>\n",
       "      <th>semantic_types</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>30982686</th>\n",
       "      <td>C0001418</td>\n",
       "      <td>Adenocarcinoma</td>\n",
       "      <td>Adenocarcinoma</td>\n",
       "      <td>[{'tui': 'T191', 'tree_number': 'B2.2.1.2.1.2'...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30982686</th>\n",
       "      <td>C0001563</td>\n",
       "      <td>Administration, Oral</td>\n",
       "      <td>Administration of drug or medicament via oral ...</td>\n",
       "      <td>[{'tui': 'T061', 'tree_number': 'B1.3.1.3', 'n...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30982686</th>\n",
       "      <td>C0001675</td>\n",
       "      <td>Adult</td>\n",
       "      <td>Adult</td>\n",
       "      <td>[{'tui': 'T100', 'tree_number': 'A2.9.4', 'nam...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30982686</th>\n",
       "      <td>C0001792</td>\n",
       "      <td>Aged</td>\n",
       "      <td>Elderly</td>\n",
       "      <td>[{'tui': 'T098', 'tree_number': 'A2.9.2', 'nam...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30982686</th>\n",
       "      <td>C0001795</td>\n",
       "      <td>Aged, 80 and over</td>\n",
       "      <td>Aged, 80 and over</td>\n",
       "      <td>[{'tui': 'T100', 'tree_number': 'A2.9.4', 'nam...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32880601</th>\n",
       "      <td>C2603343</td>\n",
       "      <td>Study</td>\n",
       "      <td>Study</td>\n",
       "      <td>[{'tui': 'T062', 'tree_number': 'B1.3.2', 'nam...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32880601</th>\n",
       "      <td>C0599755</td>\n",
       "      <td>Cohort</td>\n",
       "      <td>Cohort</td>\n",
       "      <td>[{'tui': 'T098', 'tree_number': 'A2.9.2', 'nam...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32880601</th>\n",
       "      <td>C0439234</td>\n",
       "      <td>year (qualifier value)</td>\n",
       "      <td>Year</td>\n",
       "      <td>[{'tui': 'T079', 'tree_number': 'A2.1.1', 'nam...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32880601</th>\n",
       "      <td>C0025266</td>\n",
       "      <td>Man</td>\n",
       "      <td>Man</td>\n",
       "      <td>[{'tui': 'T098', 'tree_number': 'A2.9.2', 'nam...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32880601</th>\n",
       "      <td>C0574516</td>\n",
       "      <td>Mende</td>\n",
       "      <td>Mende Language</td>\n",
       "      <td>[{'tui': 'T171', 'tree_number': 'A2.5', 'name'...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>127 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               cui                    term  \\\n",
       "id                                           \n",
       "30982686  C0001418          Adenocarcinoma   \n",
       "30982686  C0001563    Administration, Oral   \n",
       "30982686  C0001675                   Adult   \n",
       "30982686  C0001792                    Aged   \n",
       "30982686  C0001795       Aged, 80 and over   \n",
       "...            ...                     ...   \n",
       "32880601  C2603343                   Study   \n",
       "32880601  C0599755                  Cohort   \n",
       "32880601  C0439234  year (qualifier value)   \n",
       "32880601  C0025266                     Man   \n",
       "32880601  C0574516                   Mende   \n",
       "\n",
       "                                                  text_umls  \\\n",
       "id                                                            \n",
       "30982686                                     Adenocarcinoma   \n",
       "30982686  Administration of drug or medicament via oral ...   \n",
       "30982686                                              Adult   \n",
       "30982686                                            Elderly   \n",
       "30982686                                  Aged, 80 and over   \n",
       "...                                                     ...   \n",
       "32880601                                              Study   \n",
       "32880601                                             Cohort   \n",
       "32880601                                               Year   \n",
       "32880601                                                Man   \n",
       "32880601                                     Mende Language   \n",
       "\n",
       "                                             semantic_types  \n",
       "id                                                           \n",
       "30982686  [{'tui': 'T191', 'tree_number': 'B2.2.1.2.1.2'...  \n",
       "30982686  [{'tui': 'T061', 'tree_number': 'B1.3.1.3', 'n...  \n",
       "30982686  [{'tui': 'T100', 'tree_number': 'A2.9.4', 'nam...  \n",
       "30982686  [{'tui': 'T098', 'tree_number': 'A2.9.2', 'nam...  \n",
       "30982686  [{'tui': 'T100', 'tree_number': 'A2.9.4', 'nam...  \n",
       "...                                                     ...  \n",
       "32880601  [{'tui': 'T062', 'tree_number': 'B1.3.2', 'nam...  \n",
       "32880601  [{'tui': 'T098', 'tree_number': 'A2.9.2', 'nam...  \n",
       "32880601  [{'tui': 'T079', 'tree_number': 'A2.1.1', 'nam...  \n",
       "32880601  [{'tui': 'T098', 'tree_number': 'A2.9.2', 'nam...  \n",
       "32880601  [{'tui': 'T171', 'tree_number': 'A2.5', 'name'...  \n",
       "\n",
       "[127 rows x 4 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_rcts_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>UMLS Term</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pubmed ID</th>\n",
       "      <th>CUI</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"11\" valign=\"top\">30982686</th>\n",
       "      <th>C0001418</th>\n",
       "      <td>Adenocarcinoma</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C1533734</th>\n",
       "      <td>Administration</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C0001563</th>\n",
       "      <td>Administration of drug or medicament via oral ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C0003393</th>\n",
       "      <td>Antineoplastic Combined Chemotherapy Protocols</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C0038898</th>\n",
       "      <td>Digestive System Surgery</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C1332166</th>\n",
       "      <td>Gastroesophageal Junction Adenocarcinoma</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C0677984</th>\n",
       "      <td>Locally Advanced Malignant Neoplasm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C0027627</th>\n",
       "      <td>Metastasis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C0038356</th>\n",
       "      <td>Neoplasm of the stomach</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C0278498</th>\n",
       "      <td>Stage IV Gastric Cancer AJCC v7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C0087111</th>\n",
       "      <td>Therapeutic Procedure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">31595970</th>\n",
       "      <th>C0001418</th>\n",
       "      <td>Adenocarcinoma</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C0038898</th>\n",
       "      <td>Digestive System Surgery</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C0685938</th>\n",
       "      <td>Malignant gastrointestinal tract tumors</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C0017185</th>\n",
       "      <td>Neoplasm of the gastrointestinal tract</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C0543467</th>\n",
       "      <td>Surgical Procedure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">32421440</th>\n",
       "      <th>C0085198</th>\n",
       "      <td>Esophagectomy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C0282624</th>\n",
       "      <td>Minimally Invasive Surgery</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">32880601</th>\n",
       "      <th>C0877373</th>\n",
       "      <td>Advanced Malignant Neoplasm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C0003393</th>\n",
       "      <td>Antineoplastic Combined Chemotherapy Protocols</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C1708063</th>\n",
       "      <td>First-Line Therapy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C4744333</th>\n",
       "      <td>Locally Advanced Unresectable Carcinoma</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C0038356</th>\n",
       "      <td>Neoplasm of the stomach</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                            UMLS Term\n",
       "Pubmed ID CUI                                                        \n",
       "30982686  C0001418                                     Adenocarcinoma\n",
       "          C1533734                                     Administration\n",
       "          C0001563  Administration of drug or medicament via oral ...\n",
       "          C0003393     Antineoplastic Combined Chemotherapy Protocols\n",
       "          C0038898                           Digestive System Surgery\n",
       "          C1332166           Gastroesophageal Junction Adenocarcinoma\n",
       "          C0677984                Locally Advanced Malignant Neoplasm\n",
       "          C0027627                                         Metastasis\n",
       "          C0038356                            Neoplasm of the stomach\n",
       "          C0278498                    Stage IV Gastric Cancer AJCC v7\n",
       "          C0087111                              Therapeutic Procedure\n",
       "31595970  C0001418                                     Adenocarcinoma\n",
       "          C0038898                           Digestive System Surgery\n",
       "          C0685938            Malignant gastrointestinal tract tumors\n",
       "          C0017185             Neoplasm of the gastrointestinal tract\n",
       "          C0543467                                 Surgical Procedure\n",
       "32421440  C0085198                                      Esophagectomy\n",
       "          C0282624                         Minimally Invasive Surgery\n",
       "32880601  C0877373                        Advanced Malignant Neoplasm\n",
       "          C0003393     Antineoplastic Combined Chemotherapy Protocols\n",
       "          C1708063                                 First-Line Therapy\n",
       "          C4744333            Locally Advanced Unresectable Carcinoma\n",
       "          C0038356                            Neoplasm of the stomach"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from evaluation.metrics import prepare_missing_rcts_concepts_df\n",
    "\n",
    "missing_rcts_concepts_df = prepare_missing_rcts_concepts_df(\n",
    "    missing_rcts_df=missing_rcts_df\n",
    ")\n",
    "missing_rcts_concepts_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Unscreened High Quality Evidence"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this section, we consider all sources. However, we exclude duplicates that are both in Pubmed and in Trialstreamer:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_query.reset_index(drop=True, inplace=True)\n",
    "df_query = df_query[\n",
    "    (\n",
    "        ~df_query.sort_values([\"pm_id\", \"source\"]).duplicated(\n",
    "            subset=\"pm_id\", keep=\"first\"\n",
    "        )\n",
    "    )\n",
    "    | (df_query[\"pm_id\"].isnull())\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then set boolean flags with which we can filter out the already screened evidence:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from evaluation.matching import check_for_matches_in_screened\n",
    "\n",
    "df_query = check_for_matches_in_screened(\n",
    "    df_query=df_query, df_screened=df_s_with_ggponc\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_query_unscreened = df_query.query(\"is_screened == False\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>has_title_match_in_screened</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>source</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Civic</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ClinicalTrials</th>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pubmed</th>\n",
       "      <td>77</td>\n",
       "      <td>1</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>113</td>\n",
       "      <td>1</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "has_title_match_in_screened  False  True  All\n",
       "source                                       \n",
       "Civic                            2     0    2\n",
       "ClinicalTrials                  34     0   34\n",
       "Pubmed                          77     1   78\n",
       "All                            113     1  114"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(\n",
    "    df_query_unscreened[\"source\"],\n",
    "    df_query_unscreened[\"has_title_match_in_screened\"],\n",
    "    margins=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "FILTER_HQ = (\n",
    "    \"is_in_ggponc == False\"\n",
    "    \"&\"\n",
    "    \"has_title_match_in_screened == False\"\n",
    "    \"&\"\n",
    "    \"has_pediatric_population == False\"\n",
    "    \"&\"\n",
    "    \"(\"\n",
    "    \"(source == 'ClinicalTrials' & phase_int >= 3 & results_available)\"\n",
    "    \"|\"\n",
    "    \"(source == 'Civic' & evidence_rating >= 4)\"\n",
    "    \"|\"\n",
    "    \"(source == 'Pubmed' & abstract.str.len() > 1 & is_rct == True & phase_int >= 2)\"\n",
    "    \")\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>abstract</th>\n",
       "      <th>publication_date</th>\n",
       "      <th>source</th>\n",
       "      <th>pm_id</th>\n",
       "      <th>pmc_id</th>\n",
       "      <th>nct_id</th>\n",
       "      <th>referenced_nct_ids</th>\n",
       "      <th>referenced_pm_ids</th>\n",
       "      <th>phase</th>\n",
       "      <th>...</th>\n",
       "      <th>interventions_recommended</th>\n",
       "      <th>interventions_not_recommended</th>\n",
       "      <th>cpg_matches_population</th>\n",
       "      <th>cpg_matches_intervention</th>\n",
       "      <th>guideline_id</th>\n",
       "      <th>article_date_api</th>\n",
       "      <th>publication_date_combined</th>\n",
       "      <th>is_in_ggponc</th>\n",
       "      <th>is_screened</th>\n",
       "      <th>has_title_match_in_screened</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>Radiation Therapy, Paclitaxel, and Carboplatin...</td>\n",
       "      <td>This randomized phase III trial studies how we...</td>\n",
       "      <td>2021-05-04</td>\n",
       "      <td>ClinicalTrials</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>None</td>\n",
       "      <td>NCT01196390</td>\n",
       "      <td>[]</td>\n",
       "      <td>[35038433]</td>\n",
       "      <td>PHASE3</td>\n",
       "      <td>...</td>\n",
       "      <td>[chemotherapy agent, trastuzumab, antineoplast...</td>\n",
       "      <td>[carboplatin, trastuzumab biosimilar hlx02, an...</td>\n",
       "      <td>[oesophaguskarzinom]</td>\n",
       "      <td>[pomgat, palliativmedizin, endometriumkarzinom...</td>\n",
       "      <td>oesophaguskarzinom</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2021-05-04</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>356</th>\n",
       "      <td>Study of Pembrolizumab (MK-3475) Versus Invest...</td>\n",
       "      <td>In the China extension study, Chinese particip...</td>\n",
       "      <td>2020-02-26</td>\n",
       "      <td>ClinicalTrials</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>None</td>\n",
       "      <td>NCT03933449</td>\n",
       "      <td>[]</td>\n",
       "      <td>[34973513]</td>\n",
       "      <td>PHASE3</td>\n",
       "      <td>...</td>\n",
       "      <td>[chemotherapy agent, pembrolizumab, antineopla...</td>\n",
       "      <td>[irinotecan, antineoplastic agents, phytogenic...</td>\n",
       "      <td>[oesophaguskarzinom]</td>\n",
       "      <td>[pomgat, palliativmedizin, endometriumkarzinom...</td>\n",
       "      <td>oesophaguskarzinom</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2020-02-26</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 68 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 title  \\\n",
       "340  Radiation Therapy, Paclitaxel, and Carboplatin...   \n",
       "356  Study of Pembrolizumab (MK-3475) Versus Invest...   \n",
       "\n",
       "                                              abstract publication_date  \\\n",
       "340  This randomized phase III trial studies how we...       2021-05-04   \n",
       "356  In the China extension study, Chinese particip...       2020-02-26   \n",
       "\n",
       "             source  pm_id pmc_id       nct_id referenced_nct_ids  \\\n",
       "340  ClinicalTrials   <NA>   None  NCT01196390                 []   \n",
       "356  ClinicalTrials   <NA>   None  NCT03933449                 []   \n",
       "\n",
       "    referenced_pm_ids   phase  ...  \\\n",
       "340        [35038433]  PHASE3  ...   \n",
       "356        [34973513]  PHASE3  ...   \n",
       "\n",
       "                             interventions_recommended  \\\n",
       "340  [chemotherapy agent, trastuzumab, antineoplast...   \n",
       "356  [chemotherapy agent, pembrolizumab, antineopla...   \n",
       "\n",
       "                         interventions_not_recommended  \\\n",
       "340  [carboplatin, trastuzumab biosimilar hlx02, an...   \n",
       "356  [irinotecan, antineoplastic agents, phytogenic...   \n",
       "\n",
       "     cpg_matches_population  \\\n",
       "340    [oesophaguskarzinom]   \n",
       "356    [oesophaguskarzinom]   \n",
       "\n",
       "                              cpg_matches_intervention        guideline_id  \\\n",
       "340  [pomgat, palliativmedizin, endometriumkarzinom...  oesophaguskarzinom   \n",
       "356  [pomgat, palliativmedizin, endometriumkarzinom...  oesophaguskarzinom   \n",
       "\n",
       "    article_date_api publication_date_combined is_in_ggponc is_screened  \\\n",
       "340              NaT                2021-05-04        False       False   \n",
       "356              NaT                2020-02-26        False       False   \n",
       "\n",
       "    has_title_match_in_screened  \n",
       "340                       False  \n",
       "356                       False  \n",
       "\n",
       "[2 rows x 68 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_query_unscreened_hq = df_query_unscreened.query(FILTER_HQ)\n",
    "\n",
    "manual_review = df_query_unscreened_hq.sort_values(\n",
    "    [\"source\", \"publication_date\"], ascending=False\n",
    ")\n",
    "manual_review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_query_unscreened_hq.to_csv(PATH_UNSCREENED_HQ, index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Latex Output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "from evaluation.metrics import remove_last_cline_from_tex_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Missing RCTs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "if OVERWRITE_LATEX_OUTPUT:\n",
    "    missing_rcts_concepts_df[[\"UMLS Term\"]].style.format(escape=\"latex\").to_latex(\n",
    "        PATH_LATEX_TABLE_OUTPUT / f\"missing_rcts.tex\",\n",
    "        hrules=True,\n",
    "        clines=\"skip-last;data\",\n",
    "    )\n",
    "    remove_last_cline_from_tex_table(PATH_LATEX_TABLE_OUTPUT / f\"missing_rcts.tex\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Retrieval Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "if OVERWRITE_LATEX_OUTPUT:\n",
    "    for subset_screened_desc in metrics_w_manual.keys():\n",
    "        if subset_screened_desc == \"All\":\n",
    "            column_format = \"lS[table-format=1.2]S[table-format=1.2]S[table-format=1.2]S[table-format=2]S[table-format=3]S[table-format=2]S[table-format=4]S[table-format=2]S[table-format=3]\"\n",
    "        else:\n",
    "            column_format = \"lS[table-format=1.2]S[table-format=1.2]S[table-format=1.2]S[table-format=2]S[table-format=3]S[table-format=2]S[table-format=3]S[table-format=3]S[table-format=3]\"\n",
    "        highlight_metrics(\n",
    "            metrics_to_df(\n",
    "                metrics_w_manual[subset_screened_desc],\n",
    "                drop_zero_tp=False,\n",
    "                use_abbreviations=True,\n",
    "            )\n",
    "            .head(len(FILTERS_RETRIEVED))\n",
    "            .drop(columns=[\"Scr.\", \"Rel.\"])\n",
    "        ).applymap_index(lambda v: \"font-weight: bold;\", axis=\"columns\").to_latex(\n",
    "            PATH_LATEX_TABLE_OUTPUT / f\"prec_rec_{subset_screened_desc}.tex\",\n",
    "            hrules=True,\n",
    "            convert_css=True,\n",
    "            siunitx=True,\n",
    "            clines=\"all;data\",\n",
    "            column_format=column_format,\n",
    "        )\n",
    "        remove_last_cline_from_tex_table(\n",
    "            PATH_LATEX_TABLE_OUTPUT / f\"prec_rec_{subset_screened_desc}.tex\"\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 551.181x340.649 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 551.181x340.649 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if OVERWRITE_LATEX_OUTPUT:\n",
    "    for subset_screened_desc, subset_retrieved_desc in zip(\n",
    "        [\"All\", \"Only RCTs\"], [\"All\", \"Is RCT\"]\n",
    "    ):\n",
    "        fig, ax = draw_venn_rsi(\n",
    "            title=None,\n",
    "            ids_retrieved=metrics_w_manual[subset_screened_desc][subset_retrieved_desc][\n",
    "                \"ids_retrieved\"\n",
    "            ],\n",
    "            ids_screened=metrics_w_manual[subset_screened_desc][subset_retrieved_desc][\n",
    "                \"ids_screened\"\n",
    "            ],\n",
    "            ids_screened_relevant=metrics_w_manual[subset_screened_desc][\n",
    "                subset_retrieved_desc\n",
    "            ][\"ids_screened_relevant\"],\n",
    "            add_pn_labels=True,\n",
    "            circle_label_size=16,\n",
    "            label_size=14,\n",
    "            label_offsets={\"?\": (-0.05, 0.05)},\n",
    "        )\n",
    "        fig.savefig(\n",
    "            PATH_LATEX_PLOT_OUTPUT\n",
    "            / f\"venn3_screened_found_included_{subset_screened_desc}.pdf\",\n",
    "        )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Screening Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 551.181x340.649 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from evaluation.plotting import draw_docs_by_publication_date\n",
    "\n",
    "if OVERWRITE_LATEX_OUTPUT:\n",
    "    draw_docs_by_publication_date(\n",
    "        df_screened=df_s,\n",
    "        col_date=\"publication_date\",\n",
    "        col_relevant=\"is_relevant\",\n",
    "        output_dir=PATH_LATEX_PLOT_OUTPUT,\n",
    "        file_name=f\"screened_by_publication_year.pdf\",\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 551.181x340.649 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from evaluation.plotting import draw_docs_publication_types\n",
    "\n",
    "if OVERWRITE_LATEX_OUTPUT:\n",
    "    draw_docs_publication_types(\n",
    "        df_screened=df_s,\n",
    "        n_most_common=10,\n",
    "        col_ptypes=\"publication_types\",\n",
    "        col_relevant=\"is_relevant\",\n",
    "        output_dir=PATH_LATEX_PLOT_OUTPUT,\n",
    "        file_name=f\"publication_types_included.pdf\",\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Retrieved Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 551.181x340.649 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from evaluation.plotting import set_size\n",
    "from matplotlib.dates import AutoDateLocator\n",
    "\n",
    "if OVERWRITE_LATEX_OUTPUT:\n",
    "    monthly_retrievals = (\n",
    "        df_query.query(\"source != 'Trialstreamer'\")\n",
    "        .groupby([pd.Grouper(key=\"publication_date_combined\", freq=\"3M\"), \"source\"])\n",
    "        .size()\n",
    "        .unstack()\n",
    "        .rename(index=lambda d: f\"Q{pd.Timestamp(d).quarter} {d.year}\")\n",
    "        .rename_axis(\"Source\", axis=1)\n",
    "    )[[\"Pubmed\", \"ClinicalTrials\", \"Civic\"]]\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=set_size(\"thesis\"), layout=\"constrained\")\n",
    "    monthly_retrievals.plot(\n",
    "        kind=\"bar\",\n",
    "        stacked=True,\n",
    "        ax=ax,\n",
    "        xlabel=\"Quarter of the Systematic Review Time Frame\",\n",
    "        ylabel=\"Number of Retrieved Publications\",\n",
    "        rot=0,\n",
    "    )\n",
    "    locator = AutoDateLocator()\n",
    "    locator.intervald[\"QUARTER\"] = 1\n",
    "    ax.xaxis.set_major_locator(locator)\n",
    "    fig.savefig(PATH_LATEX_PLOT_OUTPUT / f\"monthly_retrievals_by_source.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 551.181x340.649 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if OVERWRITE_LATEX_OUTPUT:\n",
    "    fig, ax = plt.subplots(figsize=set_size(\"thesis\"), layout=\"constrained\")\n",
    "\n",
    "    monhtly_retrieved_vs_screened_pm = (\n",
    "        df_query.query(\"source == 'Pubmed' & is_rct_pt == True & is_in_ggponc == False\")\n",
    "        .merge(df_s[[\"pm_id\", \"is_included\"]], on=\"pm_id\", how=\"left\", indicator=True)\n",
    "        .groupby([pd.Grouper(key=\"publication_date_combined\", freq=\"3M\"), \"_merge\"])\n",
    "        .size()\n",
    "        .unstack()[[\"both\", \"left_only\"]]\n",
    "        .rename(\n",
    "            index=lambda d: f\"Q{pd.Timestamp(d).quarter} {d.year}\",\n",
    "            columns={\"left_only\": \"Unscreened\", \"both\": \"Screened\"},\n",
    "        )\n",
    "        .rename_axis(None, axis=1)\n",
    "    )\n",
    "\n",
    "    monhtly_retrieved_vs_screened_pm.plot(\n",
    "        kind=\"bar\",\n",
    "        stacked=True,\n",
    "        ylabel=\"Number of RCTs retrieved from Pubmed\",\n",
    "        xlabel=\"Quarter of the Systematic Review Time Frame\",\n",
    "        ax=ax,\n",
    "        rot=0,\n",
    "    )\n",
    "\n",
    "    locator = AutoDateLocator()\n",
    "    locator.intervald[\"QUARTER\"] = 1\n",
    "    ax.xaxis.set_major_locator(locator)\n",
    "\n",
    "    fig.savefig(PATH_LATEX_PLOT_OUTPUT / f\"monthly_retrieved_vs_screened_rcts.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 551.181x340.649 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if OVERWRITE_LATEX_OUTPUT:\n",
    "    df_query.loc[df_query.query(FILTER_HQ).index, \"is_hq\"] = True\n",
    "    df_query[\"is_hq\"] = df_query[\"is_hq\"].fillna(False)\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=set_size(\"thesis\"), layout=\"constrained\")\n",
    "    df_query.query(\"source != 'Trialstreamer'\").groupby(\n",
    "        [pd.Grouper(key=\"publication_date_combined\", freq=\"3M\"), \"is_hq\"]\n",
    "    ).size().unstack().rename(\n",
    "        index=lambda d: f\"Q{pd.Timestamp(d).quarter} {d.year}\",\n",
    "        columns={True: \"High\", False: \"Normal\"},\n",
    "    ).rename_axis(\n",
    "        \"Evidence Quality\", axis=1\n",
    "    ).plot(\n",
    "        kind=\"bar\",\n",
    "        stacked=True,\n",
    "        ax=ax,\n",
    "        xlabel=\"Month of the Systematic Review Time Frame\",\n",
    "        ylabel=\"Number of Retrieved Publications\",\n",
    "        rot=0,\n",
    "    )\n",
    "    locator = AutoDateLocator()\n",
    "    locator.intervald[\"QUARTER\"] = 1\n",
    "    ax.xaxis.set_major_locator(locator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 551.181x340.649 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if OVERWRITE_LATEX_OUTPUT:\n",
    "    df_merged = (\n",
    "        df_query.query(\"source == 'Pubmed' & is_rct_pt == True\")\n",
    "        .dropna(subset=\"pm_id\")\n",
    "        .merge(\n",
    "            df_s.query(\"is_rct_api == True\")[\n",
    "                [\"pm_id\", \"publication_date\", \"is_included\"]\n",
    "            ].dropna(subset=\"pm_id\"),\n",
    "            on=\"pm_id\",\n",
    "            how=\"outer\",\n",
    "            indicator=True,\n",
    "        )\n",
    "    )\n",
    "    df_merged[\"common_publication_date\"] = df_merged[\n",
    "        \"publication_date_combined\"\n",
    "    ].fillna(df_merged[\"publication_date_y\"])\n",
    "\n",
    "    montly_retrieved_vs_screened = (\n",
    "        df_merged.groupby(\n",
    "            [pd.Grouper(key=\"common_publication_date\", freq=\"3M\"), \"_merge\"]\n",
    "        )\n",
    "        .size()\n",
    "        .unstack()\n",
    "        .rename(\n",
    "            index=lambda d: f\"Q{pd.Timestamp(d).quarter} {d.year}\",\n",
    "            columns={\n",
    "                \"left_only\": \"Only Retrieved\",\n",
    "                \"both\": \"Screened and Retrieved\",\n",
    "                \"right_only\": \"Only Screened\",\n",
    "            },\n",
    "        )\n",
    "        .rename_axis(None, axis=1)\n",
    "    )[[\"Only Screened\", \"Screened and Retrieved\", \"Only Retrieved\"]]\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=set_size(\"thesis\"), layout=\"constrained\")\n",
    "    montly_retrieved_vs_screened.plot(\n",
    "        kind=\"bar\",\n",
    "        stacked=True,\n",
    "        ax=ax,\n",
    "        xlabel=\"Year of the Systematic Review Time Frame\",\n",
    "        ylabel=\"Number of RCTs\",\n",
    "        rot=0,\n",
    "    )\n",
    "    locator = AutoDateLocator()\n",
    "    locator.intervald[\"QUARTER\"] = 1\n",
    "    ax.xaxis.set_major_locator(locator)\n",
    "\n",
    "    fig.savefig(PATH_LATEX_PLOT_OUTPUT / f\"yearly_retrieved_vs_screened_rcts.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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