{"id":"W4297491332","doi":"10.3389/fcvm.2022.998558","title":"Machine learning prediction of atrial fibrillation in cardiovascular patients using cardiac magnetic resonance and electronic health information","year":2022,"lang":"en","type":"article","venue":"Frontiers in Cardiovascular Medicine","topic":"Atrial Fibrillation Management and Outcomes","field":"Medicine","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Libin Cardiovascular Institute of Alberta; University of Calgary","funders":"Circle Cardiovascular Imaging; Alberta Innovates; Siemens Healthineers; Pfizer; Amgen","keywords":"Atrial fibrillation; Cardiology; Medicine; Internal medicine; Cardiac magnetic resonance; Magnetic resonance imaging; Cardiovascular health; Disease; Radiology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.004337501,0.0005914676,0.000614744,0.001244259,0.000192365,0.0006602184,0.0004780726,0.0005250378,0.0007212335],"category_scores_gemma":[0.01233121,0.0001612694,0.0007474234,0.000722803,0.0001967354,0.0005225203,0.0004125246,0.000820609,0.0002376031],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005466322,"about_ca_system_score_gemma":0.0007760566,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006601894,"about_ca_topic_score_gemma":0.005148848,"domain_scores_codex":[0.999285,0.0003234106,0.00006340426,0.0001547244,0.00009973749,0.0000737071],"domain_scores_gemma":[0.991659,0.006320559,0.0008577293,0.0002770711,0.0006262839,0.0002593281],"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":[0.0007090777,0.0004971789,0.7256956,0.00006113224,0.0003986938,0.0001741253,0.00008513458,0.2059509,0.0003716884,0.0002657912,0.001409712,0.06438093],"study_design_scores_gemma":[0.00004812122,0.0003958546,0.09925413,0.00003888155,0.0001171819,0.0001605533,0.00004346685,0.8977755,0.0004914072,0.001251669,0.0004023738,0.0000208465],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9797676,0.0005516363,0.01738051,0.0005757951,0.00003963415,0.00004999878,0.0009017983,0.0001269353,0.0006061713],"genre_scores_gemma":[0.9954514,0.00009411413,0.003484276,0.00004633893,0.000027225,0.00001772915,0.0007520075,0.000003668721,0.0001232441],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006601894,"threshold_uncertainty_score":0.02293921,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01443400043170206,"score_gpt":0.2427830939318296,"score_spread":0.2283490935001276,"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."}}