{"id":"W2985214693","doi":"10.1002/clc.23283","title":"Predicting risk of cardiovascular events 1 to 3 years post‐myocardial infarction using a global registry","year":2019,"lang":"en","type":"article","venue":"Clinical Cardiology","topic":"Acute Myocardial Infarction Research","field":"Medicine","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; St. Michael's Hospital","funders":"AstraZeneca","keywords":"Medicine; Myocardial infarction; Internal medicine; Stroke (engine); Kidney disease; Unstable angina; Cardiology; Coronary artery disease; Revascularization; Diabetes mellitus; Angina; Framingham Risk Score; Risk factor; Poisson regression; Disease; Population","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":[0.002830713,0.0004245892,0.0004979624,0.001508703,0.0002062084,0.0007417956,0.0005198398,0.00033995,0.0007718318],"category_scores_gemma":[0.004878546,0.0002241136,0.0006813941,0.00193188,0.0002433156,0.0008387907,0.001025672,0.0005685702,0.0002588989],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002736236,"about_ca_system_score_gemma":0.000536531,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003459614,"about_ca_topic_score_gemma":0.005081604,"domain_scores_codex":[0.9989949,0.0004930577,0.0001073203,0.00017868,0.0001277736,0.00009824924],"domain_scores_gemma":[0.9961888,0.0006256679,0.002067925,0.00041671,0.0003271651,0.0003738045],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004408428,0.00001533759,0.998406,0.000008866511,0.00004533599,0.00001015618,0.00001545464,0.0001139964,0.00001705084,0.00002252717,0.0002040401,0.001097112],"study_design_scores_gemma":[0.00002395365,0.0001035851,0.9980149,0.00001908115,0.00009732904,0.00008937039,0.00008762585,0.001149864,0.00004756603,0.00007839844,0.0002820567,0.00000616317],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9937583,0.0002744155,0.0006479443,0.0002843611,0.00001173714,0.00006224546,0.00393915,0.00002823736,0.0009935662],"genre_scores_gemma":[0.9955103,0.0001742358,0.0006721908,0.00004610035,0.00002444292,0.00005930771,0.003425007,0.000004414578,0.00008386002],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003459614,"threshold_uncertainty_score":0.01497042,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0481388989360589,"score_gpt":0.3870588504372379,"score_spread":0.338919951501179,"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."}}