{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0001737666,0.0002950483,0.0003090017,0.00009845259,0.0002814639,0.00001229734,0.0003583633,0.0001318536,0.001067284],"category_scores_gemma":[0.00008494322,0.0002707451,0.0001699376,0.0003293194,0.00009676891,0.0004912153,0.0001347695,0.0008826432,0.00560408],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005854174,"about_ca_system_score_gemma":0.00009296767,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004885133,"about_ca_topic_score_gemma":0.000001389869,"domain_scores_codex":[0.9984419,0.0001315615,0.0004851281,0.0002190919,0.0002687623,0.0004536069],"domain_scores_gemma":[0.9986428,0.0002610338,0.0002194502,0.0002441514,0.0000829545,0.0005495966],"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":[0.002467971,0.001690373,0.005966472,0.001264119,0.001941317,0.0003430433,0.01755072,0.02655151,0.03570467,0.005898004,0.8775103,0.02311151],"study_design_scores_gemma":[0.002826292,0.00009146526,0.0004183809,0.00002943229,0.0002426895,0.00001613318,0.001429214,0.05887735,0.01914528,0.00004097811,0.9164658,0.0004170092],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8776074,0.0004798761,0.002024345,0.01699285,0.004854858,0.002689593,0.003255163,0.00128184,0.09081405],"genre_scores_gemma":[0.9469427,0.0004067659,0.001477114,0.04939651,0.0005320361,0.00005608912,0.0003996871,0.00003824658,0.0007508248],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09006322,"threshold_uncertainty_score":0.9999745,"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."}}