{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001713822,0.0002506334,0.0006854364,0.0002363078,0.0002987373,0.000003555749,0.0002042097,0.00007941001,0.0007664041],"category_scores_gemma":[0.0007915241,0.0002028362,0.0003252668,0.0005856812,0.0001640578,0.0001968059,0.0003185823,0.001259184,0.00001653253],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001481651,"about_ca_system_score_gemma":0.0002421759,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004880831,"about_ca_topic_score_gemma":0.000006312706,"domain_scores_codex":[0.9945995,0.0002985215,0.001193025,0.0001998993,0.003468596,0.0002405025],"domain_scores_gemma":[0.9985712,0.0001190815,0.0003820042,0.0004603103,0.0001988505,0.0002685359],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005417265,0.001146085,0.9701317,0.0006139987,0.001873832,0.0002037029,0.01976205,0.002212412,0.000001623602,0.00001543874,0.000782691,0.00271476],"study_design_scores_gemma":[0.009221266,0.002698304,0.9414064,0.0003044032,0.001438491,0.0004920855,0.0145525,0.02467046,0.000004907538,0.00004910687,0.004914763,0.0002472946],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9946237,0.0009956656,0.00106548,0.0001349972,0.0005321986,0.001764342,0.0003934633,0.0001346493,0.0003554593],"genre_scores_gemma":[0.9977048,0.0001379512,0.000433397,0.0002202895,0.0001640812,0.0006449441,0.0005944921,0.0000196675,0.0000803584],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02872526,"threshold_uncertainty_score":0.8391587,"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."}}