{"id":"W2806036031","doi":"","title":"Extracting and Normalizing Adverse Drug Reactions from Drug Labels.","year":2017,"lang":"en","type":"article","venue":"Theory and applications of categories","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Drug; Drug reaction; Computer science; Adverse drug reaction; 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":[0.002642212,0.0008018864,0.0008593341,0.01259828,0.0008317865,0.001983872,0.001074621,0.0008608912,0.00381693],"category_scores_gemma":[0.01601117,0.0002528625,0.001299186,0.008004058,0.0005099322,0.001604528,0.001633309,0.001024165,0.003393018],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009016349,"about_ca_system_score_gemma":0.003791513,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006294332,"about_ca_topic_score_gemma":0.009753747,"domain_scores_codex":[0.996635,0.000629968,0.0005472865,0.0007592475,0.001216091,0.0002123781],"domain_scores_gemma":[0.9916728,0.003386541,0.001289275,0.0009292455,0.00250406,0.0002180969],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005148573,0.0003214302,0.03598789,0.002266616,0.000374152,0.0007492545,0.0006591044,0.003185833,0.01894242,0.01397016,0.05664895,0.8663794],"study_design_scores_gemma":[0.0002462222,0.0005066656,0.1289425,0.001795713,0.001637102,0.004725155,0.003976427,0.1240501,0.1002545,0.1522814,0.4812776,0.0003066241],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1282374,0.009504272,0.7050428,0.002817818,0.001282177,0.002163388,0.1051671,0.01655484,0.0292302],"genre_scores_gemma":[0.2805107,0.003409535,0.5901183,0.0005325716,0.0003819073,0.001220192,0.115631,0.0008208453,0.007374874],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01259828,"threshold_uncertainty_score":0.01397353,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01056439242488788,"score_gpt":0.2759563180880878,"score_spread":0.2653919256631999,"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."}}