{"id":"W4224267054","doi":"10.2196/33395","title":"Risk Prediction of Major Adverse Cardiovascular Events Occurrence Within 6 Months After Coronary Revascularization: Machine Learning Study","year":2022,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Traditional Chinese Medicine Studies","field":"Medicine","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Government of Jiangsu Province; National Natural Science Foundation of China; National Science Foundation","keywords":"Mace; Medicine; Receiver operating characteristic; Revascularization; Percutaneous coronary intervention; Internal medicine; Logistic regression; Angina; Random forest; Machine learning; Cardiology; Myocardial infarction; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005632353,0.0005688908,0.0005787285,0.001152953,0.0004373065,0.0006735117,0.0007221506,0.0009489996,0.001083747],"category_scores_gemma":[0.009697146,0.000302377,0.001087618,0.0009667529,0.0004004383,0.0007757687,0.0004330887,0.001175499,0.0002594712],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000306244,"about_ca_system_score_gemma":0.000516706,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00146403,"about_ca_topic_score_gemma":0.0009749978,"domain_scores_codex":[0.9985155,0.0006591144,0.0001467135,0.0003565248,0.0002033163,0.0001187018],"domain_scores_gemma":[0.9937537,0.003580561,0.001214341,0.000449165,0.0005546009,0.0004475545],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004209963,0.0002044023,0.9953681,0.00002299334,0.000163194,0.00006510529,0.00004863084,0.0006949292,0.00006754518,0.00003062382,0.0001159916,0.002797384],"study_design_scores_gemma":[0.00006589097,0.001027654,0.9729813,0.00003234941,0.0003701696,0.0004122786,0.0001660261,0.02430429,0.0001262958,0.000214324,0.0002818423,0.00001762758],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9978847,0.0004858093,0.001096595,0.00009469051,0.00001993235,0.00003411752,0.0001339871,0.000007766697,0.0002423235],"genre_scores_gemma":[0.9988062,0.0002067377,0.000512579,0.00003094299,0.00003605051,0.00003070493,0.000273944,0.000002372366,0.000100377],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005632353,"threshold_uncertainty_score":0.02978712,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01264262395264766,"score_gpt":0.2564991319851013,"score_spread":0.2438565080324536,"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."}}