{"id":"W2513302822","doi":"10.1371/journal.pone.0160713","title":"Can We Predict Individual Combined Benefit and Harm of Therapy? Warfarin Therapy for Atrial Fibrillation as a Test Case","year":2016,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Atrial Fibrillation Management and Outcomes","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Joseph’s Healthcare Hamilton; McMaster University; St. Joseph's Hospital","funders":"China Scholarship Council; Kaiser Permanente","keywords":"Warfarin; Medicine; Atrial fibrillation; Logistic regression; Proportional hazards model; Stroke (engine); Cohort; Internal medicine; Harm; Cohort study; Emergency medicine; Intensive care medicine; Psychology","routes":{"ca_aff":true,"ca_fund":false,"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":[0.01286196,0.000940343,0.001043231,0.001505084,0.0003227056,0.001983045,0.001406843,0.001235817,0.001562648],"category_scores_gemma":[0.06130882,0.0003026899,0.001102181,0.0009290201,0.0007337833,0.00132615,0.001127988,0.001184639,0.0004269775],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007222511,"about_ca_system_score_gemma":0.001074233,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003769294,"about_ca_topic_score_gemma":0.0035948,"domain_scores_codex":[0.9937662,0.004214073,0.0003271807,0.000757929,0.0007162601,0.0002182984],"domain_scores_gemma":[0.9777398,0.01683234,0.002975758,0.001207191,0.0008477931,0.000397174],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005085296,0.0002462874,0.9308972,0.00007112498,0.0006170928,0.0005715151,0.0002206854,0.02958198,0.0003613881,0.001782607,0.001783102,0.03335849],"study_design_scores_gemma":[0.0001860581,0.0008037919,0.1681488,0.0001328141,0.0004834589,0.001330058,0.0004400069,0.8102319,0.00144808,0.01486663,0.001855682,0.00007274654],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8769314,0.0008474891,0.1106203,0.005920093,0.0001338518,0.0002043842,0.001787479,0.0002596918,0.003295396],"genre_scores_gemma":[0.9795362,0.0001439269,0.0188929,0.0002337626,0.00006773656,0.00009224673,0.0007222928,0.00001536529,0.0002955284],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01286196,"threshold_uncertainty_score":0.06802142,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1214683393760916,"score_gpt":0.3103344483912724,"score_spread":0.1888661090151808,"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."}}