{"id":"W2807245675","doi":"","title":"Overview of the TAC 2017 Adverse Reaction Extraction from Drug Labels Track.","year":2017,"lang":"en","type":"article","venue":"Theory and applications of categories","topic":"Pharmacovigilance and Adverse Drug Reactions","field":"Pharmacology, Toxicology and Pharmaceutics","cited_by":44,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Track (disk drive); Extraction (chemistry); Adverse drug reaction; Computer science; Drug; Drug reaction; Chemistry; Chromatography; Pharmacology; Medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.005482958,0.001494222,0.001399058,0.01803724,0.00120714,0.004676399,0.002460804,0.001790646,0.01346397],"category_scores_gemma":[0.02088068,0.0006993735,0.002443753,0.009173627,0.0005033293,0.003745606,0.003046722,0.001680905,0.01610782],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001855651,"about_ca_system_score_gemma":0.00705039,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02885366,"about_ca_topic_score_gemma":0.03360754,"domain_scores_codex":[0.9946936,0.0007420402,0.0008472801,0.000954318,0.00245183,0.0003110506],"domain_scores_gemma":[0.9858949,0.004241138,0.001266377,0.002863661,0.005208668,0.000525253],"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":[0.0004987352,0.0002154479,0.01702512,0.003428135,0.0005468985,0.0005845521,0.0003311379,0.004727464,0.008375096,0.01414493,0.5086188,0.4415036],"study_design_scores_gemma":[0.0001263619,0.0002061341,0.01814334,0.001397026,0.0005073668,0.001410207,0.0002486853,0.04485491,0.01487825,0.02136455,0.8966668,0.0001962915],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"review","genre_scores_codex":[0.01240848,0.01590448,0.3661532,0.003865821,0.00119319,0.001861387,0.4643507,0.09579208,0.03847069],"genre_scores_gemma":[0.03062289,0.005067474,0.2475607,0.001355006,0.0003786352,0.0009116756,0.7008898,0.002612322,0.01060158],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.02885366,"threshold_uncertainty_score":0.05737144,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09181320740700182,"score_gpt":0.4424480607818821,"score_spread":0.3506348533748803,"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."}}