{"id":"W4311281764","doi":"10.1007/s00330-022-09323-z","title":"Prediction of incident cardiovascular events using machine learning and CMR radiomics","year":2022,"lang":"en","type":"article","venue":"European Radiology","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Engineering and Physical Sciences Research Council; Universitat de Barcelona; British Heart Foundation; Medical Research Council; National Institute for Health and Care Research","keywords":"Medicine; Cardiology; Ventricle; Internal medicine; Atrial fibrillation; Neuroradiology; Magnetic resonance imaging; Heart failure; Myocardial infarction; Stroke (engine); Radiology; Neurology","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":[0.00172691,0.0001335298,0.0004388258,0.0001849102,0.00022998,0.000004529782,0.000104851,0.00002761753,0.00008382325],"category_scores_gemma":[0.0003116581,0.0001314585,0.0001649747,0.0001521083,0.0001236094,0.00003067452,0.0002458033,0.000736343,0.000002265968],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001052421,"about_ca_system_score_gemma":0.00003999202,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007980425,"about_ca_topic_score_gemma":2.637139e-7,"domain_scores_codex":[0.9978463,0.001011763,0.0003485793,0.0003083354,0.0002623479,0.0002226481],"domain_scores_gemma":[0.9994211,0.00006190809,0.0001476959,0.0002170279,0.00003069241,0.0001215911],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002901036,0.0001519223,0.8794686,0.0001686689,0.001141682,0.000818837,0.001291383,0.02910326,0.05535655,0.0002022155,0.0003078926,0.03169894],"study_design_scores_gemma":[0.008242642,0.001902911,0.4216494,0.0001100409,0.001159657,0.02307679,0.0005134696,0.3150786,0.0002344908,0.000116225,0.2274694,0.0004463203],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9891392,0.005930561,0.003013635,0.0001709601,0.0004010788,0.0001870484,0.00001204963,0.00006147462,0.001084017],"genre_scores_gemma":[0.9966434,0.0004865032,0.002112721,0.0001924287,0.0002325104,0.000003351373,0.00007071585,0.00005039621,0.0002079204],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4578192,"threshold_uncertainty_score":0.5360723,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01940795848617021,"score_gpt":0.2529520359804782,"score_spread":0.233544077494308,"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."}}