{"id":"W3104008342","doi":"10.1093/bioinformatics/btaa973","title":"MetaADEDB 2.0: a comprehensive database on adverse drug events","year":2020,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Pharmacovigilance and Adverse Drug Reactions","field":"Pharmacology, Toxicology and Pharmaceutics","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Key Research and Development Program of China; China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Adverse Event Reporting System; Computer science; Database; Drug; Online database; Drug reaction; Adverse effect; Information retrieval; World Wide Web; Data mining; Medicine; Pharmacology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006998204,0.001639705,0.003392844,0.02106933,0.0006144757,0.003996063,0.003347285,0.001661736,0.06862343],"category_scores_gemma":[0.04034787,0.001332893,0.002555219,0.0175244,0.0004266636,0.003296225,0.003796466,0.001905772,0.02409606],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001178845,"about_ca_system_score_gemma":0.005528172,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004676644,"about_ca_topic_score_gemma":0.004847838,"domain_scores_codex":[0.9939362,0.001553667,0.002434847,0.0007532993,0.001093768,0.0002282275],"domain_scores_gemma":[0.9692025,0.01465221,0.007497018,0.003040524,0.003444743,0.002163],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.003009033,0.0001814126,0.02327156,0.02214779,0.00226421,0.0006274288,0.0004744139,0.00232428,0.002488575,0.006423235,0.7981955,0.1385925],"study_design_scores_gemma":[0.001266856,0.0001988628,0.03340298,0.003158523,0.001593483,0.001131476,0.0002082461,0.003338536,0.002257706,0.009670735,0.9434421,0.0003304718],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"software","genre_scores_codex":[0.002679651,0.002959221,0.007336624,0.0009468328,0.0001225386,0.0004551728,0.9702668,0.01089451,0.004338686],"genre_scores_gemma":[0.01831703,0.002956214,0.02457144,0.0008571651,0.0002517005,0.001836757,0.9466918,0.002707258,0.001810694],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.06862343,"threshold_uncertainty_score":0.2295682,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1810652966885001,"score_gpt":0.4259213948965154,"score_spread":0.2448560982080153,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}