{"id":"W2158718916","doi":"10.1136/amiajnl-2011-000735","title":"A secure distributed logistic regression protocol for the detection of rare adverse drug events","year":2012,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Pharmacovigilance and Adverse Drug Reactions","field":"Pharmacology, Toxicology and Pharmaceutics","cited_by":72,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute for Clinical Evaluative Sciences; McGill University; Memorial University of Newfoundland; Agricultural Research Institute of Ontario; University of Ottawa","funders":"U.S. National Library of Medicine; Canadian Institutes of Health Research; Canada Research Chairs; Ontario Institute for Cancer Research; National Institutes of Health; National Science Foundation","keywords":"Computer science; Pooling; Protocol (science); Logistic regression; Data mining; Population; False positive paradox; Artificial intelligence; Machine learning; Medicine","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01434148,0.0008973222,0.00112076,0.001063208,0.001463458,0.002452437,0.002743028,0.001857504,0.004595312],"category_scores_gemma":[0.03437227,0.0006899827,0.001111676,0.0008100202,0.001903072,0.004305555,0.006597193,0.002735063,0.00167167],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001502441,"about_ca_system_score_gemma":0.004849568,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008717731,"about_ca_topic_score_gemma":0.0005383572,"domain_scores_codex":[0.9857972,0.006762831,0.001415532,0.001868286,0.003372628,0.0007835817],"domain_scores_gemma":[0.9767529,0.01177514,0.002418802,0.005794811,0.002726897,0.0005314738],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.004040773,0.0007041307,0.008674922,0.0009817407,0.0004335013,0.00368423,0.00237274,0.1981945,0.04825136,0.397833,0.02688252,0.3079466],"study_design_scores_gemma":[0.0006446007,0.000476565,0.0007010379,0.0001066257,0.000095189,0.00105238,0.0002068416,0.8541173,0.02037906,0.1053365,0.01675602,0.0001278618],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008553476,0.00008713959,0.9867104,0.0006991744,0.00007353392,0.000620639,0.0001779622,0.001565292,0.001512385],"genre_scores_gemma":[0.5868473,0.0002983825,0.4017409,0.0006975416,0.000181957,0.004284038,0.0009313269,0.0002403513,0.004778326],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01434148,"threshold_uncertainty_score":0.07584596,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07351165846192852,"score_gpt":0.4595053046842186,"score_spread":0.3859936462222901,"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."}}