{"id":"W2326081763","doi":"10.1016/j.cct.2016.04.001","title":"The win ratio approach to analyzing composite outcomes: An application to the EVOLVE trial","year":2016,"lang":"en","type":"article","venue":"Contemporary Clinical Trials","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":26,"is_retracted":false,"has_abstract":false,"ca_institutions":"St. John’s Health Sciences Centre","funders":"Amgen","keywords":"Medicine; Hazard ratio; Clinical endpoint; Placebo; Myocardial infarction; Confidence interval; Randomization; Randomized controlled trial; Internal medicine; Alternative medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"observational","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":[],"domain":null,"study_design":"design_other","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"}],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.1360275,0.0005136763,0.00303528,0.0001038683,0.0005667164,0.0003352732,0.002078078,0.0004532476,0.00004740876],"category_scores_gemma":[0.5724475,0.0002110745,0.001172104,0.000618535,0.0004238231,0.0002273607,0.0004397013,0.0005805833,0.0004120811],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008778743,"about_ca_system_score_gemma":0.0003034801,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001999213,"about_ca_topic_score_gemma":0.0000116157,"domain_scores_codex":[0.9586014,0.02951346,0.00869768,0.001444375,0.00106613,0.0006769523],"domain_scores_gemma":[0.520358,0.4730153,0.00206876,0.003182002,0.0004412562,0.0009346764],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.08730465,0.003256357,0.00428936,0.00005684492,0.001637898,0.000007054203,0.0004126907,0.00001913174,0.001119118,0.2661616,0.1532527,0.4824826],"study_design_scores_gemma":[0.07149219,0.003109771,0.003767041,0.0002085752,0.0006204522,0.000002141895,0.0002330717,0.0006096637,0.0003633391,0.8123774,0.1060994,0.001116962],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01104944,0.0000896717,0.9413995,0.02864164,0.003391138,0.01226234,0.0002305367,0.0002630052,0.002672681],"genre_scores_gemma":[0.5102688,0.00007241956,0.4681332,0.006807111,0.009104523,0.003332065,0.00001595826,0.000184113,0.002081863],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5462158,"threshold_uncertainty_score":0.8896415,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.8144677085776505,"score_gpt":0.6428355406033506,"score_spread":0.1716321679742999,"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."}}