{"id":"W2883464116","doi":"10.1093/eurheartj/ehy404","title":"Clinical applications of machine learning in cardiovascular disease and its relevance to cardiac imaging","year":2018,"lang":"en","type":"review","venue":"European Heart Journal","topic":"Cardiac Imaging and Diagnostics","field":"Medicine","cited_by":539,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Medicine; Coronary artery disease; Machine learning; Relevance (law); Modalities; Artificial intelligence; Disease; Coronary angiography; Cardiac imaging; Cardiology; Internal medicine; Computer science; Myocardial infarction","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":[0.00353095,0.0002597748,0.001828973,0.000303752,0.000101015,0.00004404848,0.0001148632,0.00003965053,0.000008241679],"category_scores_gemma":[0.002739611,0.0002234167,0.001367323,0.0003201307,0.00007781239,0.00004641336,0.000162765,0.001236903,0.0001493201],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005950368,"about_ca_system_score_gemma":0.000274121,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003495009,"about_ca_topic_score_gemma":1.007902e-7,"domain_scores_codex":[0.9967722,0.001295986,0.0008815096,0.000416856,0.0003602428,0.0002732287],"domain_scores_gemma":[0.9979683,0.0005301809,0.0002138987,0.000475952,0.0001868699,0.000624795],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002081547,0.00006301073,0.03742903,0.003541531,0.0005148381,0.0004196196,0.00003797398,0.00001801406,3.461171e-7,0.000004672994,0.003761851,0.9541883],"study_design_scores_gemma":[0.0003096381,0.0000499197,0.01215856,0.01147555,0.00209388,0.0005732261,0.00000564429,0.00002130397,1.205263e-7,0.000001730082,0.9731286,0.0001818195],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00007976119,0.9974942,0.0003211882,0.0001350398,0.0003340082,0.0006344914,0.0000373447,0.00002915676,0.0009347646],"genre_scores_gemma":[0.0005347775,0.9960181,0.0007184689,0.0001180866,0.002257623,0.0000132587,0.00002472116,0.00009620957,0.0002188262],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9693667,"threshold_uncertainty_score":0.911067,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05741497653989861,"score_gpt":0.3820033057549449,"score_spread":0.3245883292150463,"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."}}