{"id":"W4306320789","doi":"10.1093/eurheartj/ehac544.207","title":"Artificial intelligence-enabled comprehensive coronary phenotyping in patients with suspected CAD","year":2022,"lang":"en","type":"article","venue":"European Heart Journal","topic":"Coronary Interventions and Diagnostics","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Medicine; Dyslipidemia; CAD; Angina; Cohort; Internal medicine; Stenosis; Coronary artery disease; Machine learning; Cardiology; Myocardial infarction; Obesity","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.000948633,0.0003043131,0.0003019198,0.001318573,0.0001299148,0.0005903401,0.0002832832,0.0004274945,0.0008299086],"category_scores_gemma":[0.002908041,0.0001188617,0.0001852962,0.0006527664,0.0001711115,0.0001893227,0.000328856,0.0002336724,0.0001917678],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002325136,"about_ca_system_score_gemma":0.0002226387,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001364243,"about_ca_topic_score_gemma":0.001884224,"domain_scores_codex":[0.9994476,0.0002936315,0.00005226978,0.00007569088,0.0000925338,0.00003810589],"domain_scores_gemma":[0.9985297,0.0007618006,0.0002761186,0.00008717339,0.0002180059,0.0001272518],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006310288,0.0001083088,0.9612101,0.00006141971,0.00006190633,0.000597419,0.00009906349,0.002029802,0.002290081,0.000103087,0.0006114873,0.03219626],"study_design_scores_gemma":[0.00004368053,0.0005926798,0.9503357,0.00004640324,0.00008806752,0.002242437,0.0001451895,0.04354576,0.001643768,0.0005086646,0.0007871353,0.00002047548],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9948788,0.0007929621,0.00233164,0.0002370104,0.00001233072,0.00002289238,0.0003131837,0.00008406954,0.001327161],"genre_scores_gemma":[0.9975541,0.0001285041,0.001991325,0.00003778251,0.00001873346,0.000009099613,0.0001608748,0.000002698623,0.00009677905],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001364243,"threshold_uncertainty_score":0.005016923,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04845573945799825,"score_gpt":0.2906964736119148,"score_spread":0.2422407341539166,"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."}}