{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003092325,0.0002077943,0.001576346,0.0001335998,0.0000556981,0.000007851524,0.0002304179,0.0005652354,0.00004948264],"category_scores_gemma":[0.003897779,0.0002037777,0.001901316,0.0004974098,0.0002217492,0.0000688726,0.0003538861,0.0007718274,0.000279797],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002490362,"about_ca_system_score_gemma":0.0004010042,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009112015,"about_ca_topic_score_gemma":0.000001609004,"domain_scores_codex":[0.9960166,0.001113651,0.0009659862,0.0007141043,0.000682626,0.0005070722],"domain_scores_gemma":[0.9971653,0.0003802075,0.0002381048,0.001241136,0.0005739906,0.0004013095],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009016159,0.00003685609,0.9812614,0.00003601116,0.005072334,0.00005236669,0.00002180283,0.005414988,0.0004208189,0.00001791078,0.001059011,0.005704904],"study_design_scores_gemma":[0.002316041,0.001189399,0.9549472,0.00005500247,0.001200196,0.0003979917,0.0001284251,0.0004642431,0.00001717765,0.00006029018,0.03904305,0.0001809694],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9916462,0.0003533617,0.001497022,0.0004236426,0.002221496,0.001008211,0.0001594904,0.00007375258,0.002616775],"genre_scores_gemma":[0.99564,0.0002614877,0.001199238,0.001067623,0.001684164,0.0000184149,0.00003837624,0.00003333339,0.0000573326],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03798404,"threshold_uncertainty_score":0.8309811,"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."}}