{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002968606,0.00007562914,0.00009630383,0.00001686873,0.00044825,0.00002668221,0.0001426258,0.00005791502,0.000008846809],"category_scores_gemma":[0.0001550677,0.00006733003,0.00002124245,0.00001788249,0.0005849371,0.00001017973,0.0001177057,0.00006574883,0.000001515332],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000001640785,"about_ca_system_score_gemma":0.00001657225,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001702507,"about_ca_topic_score_gemma":0.00003572563,"domain_scores_codex":[0.9995363,0.00003521599,0.0001261184,0.0001725826,0.00004203698,0.00008779031],"domain_scores_gemma":[0.9993353,0.0001007068,0.0001420084,0.000341849,0.00003589049,0.00004420861],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003276408,0.0001653595,0.02078462,0.0001549304,0.0002328474,0.000002041855,0.002590316,0.000004899913,0.3775424,0.2633505,0.001260555,0.333584],"study_design_scores_gemma":[0.001058884,0.00007528185,0.0420259,0.00007181835,0.0001672101,0.00002427684,0.01500875,0.00002151797,0.3489967,0.1803998,0.4116097,0.0005401751],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9900889,0.00264939,0.003662803,0.0003381857,0.00004079954,0.00009867537,0.00003605627,0.0000188442,0.003066393],"genre_scores_gemma":[0.9968926,0.0006195563,0.001242689,0.00002857551,0.0001136804,0.00003677819,0.00003407934,0.000006168953,0.00102585],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4103491,"threshold_uncertainty_score":0.3447622,"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."}}