{"id":"W2806534782","doi":"","title":"UTH_CCB System for Adverse Drug Reaction Extraction from Drug Labels at TAC-ADR 2017.","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":17,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Drug; Adverse drug reaction; Extraction (chemistry); Computer science; Pharmacology; Chemistry; Medicine; Chromatography","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":[0.004654657,0.001387879,0.001403393,0.008767308,0.0007596289,0.003281025,0.002082694,0.001992659,0.1071002],"category_scores_gemma":[0.02226089,0.0007634942,0.001524283,0.004609968,0.0004936815,0.002554873,0.002969977,0.0011739,0.07171296],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001467531,"about_ca_system_score_gemma":0.004240506,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007361992,"about_ca_topic_score_gemma":0.004496059,"domain_scores_codex":[0.9971239,0.0006918691,0.0005892393,0.000506672,0.0008567991,0.0002315925],"domain_scores_gemma":[0.9868655,0.004583248,0.00179549,0.003310418,0.003022618,0.0004227478],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002201362,0.0001881653,0.01478403,0.003572547,0.0004842069,0.0006708342,0.0004247006,0.003208246,0.007761883,0.01499249,0.7476753,0.2040363],"study_design_scores_gemma":[0.0004727453,0.000206561,0.0187912,0.0009434612,0.0002328593,0.001133339,0.0001504398,0.02239924,0.02686932,0.01871807,0.9098551,0.000227711],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"methods","genre_scores_codex":[0.009183854,0.003000075,0.1261306,0.001772417,0.0006428276,0.001181754,0.5234215,0.2990197,0.03564727],"genre_scores_gemma":[0.08082321,0.002273174,0.198286,0.002898224,0.0005891998,0.002439583,0.6660665,0.02201147,0.02461266],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.1071002,"threshold_uncertainty_score":0.3582857,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05518476789096707,"score_gpt":0.411657228408309,"score_spread":0.3564724605173419,"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."}}