{"id":"W4284896820","doi":"10.1016/j.jcct.2022.07.002","title":"CAD-RADS™ 2.0 - 2022 Coronary Artery Disease-Reporting and Data System","year":2022,"lang":"en","type":"article","venue":"Journal of cardiovascular computed tomography","topic":"Cardiac Imaging and Diagnostics","field":"Medicine","cited_by":368,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"British Heart Foundation","keywords":"Medicine; Coronary artery disease; CAD; Stenosis; Fractional flow reserve; Radiology; Internal medicine; Angiography; Cardiology; Coronary angiography; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006006934,0.0009549159,0.001671441,0.004167628,0.0006795251,0.003710319,0.002480502,0.001951066,0.1347867],"category_scores_gemma":[0.02272232,0.000910249,0.001082329,0.004043749,0.0004107417,0.00194128,0.002201194,0.00191941,0.1464398],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001499832,"about_ca_system_score_gemma":0.004686044,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007776963,"about_ca_topic_score_gemma":0.00453247,"domain_scores_codex":[0.9962459,0.0009935733,0.0008008281,0.0005453686,0.0008588982,0.0005554302],"domain_scores_gemma":[0.9786661,0.004992329,0.002208155,0.003052108,0.00770932,0.003371995],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0007472969,0.00007633361,0.003746161,0.0003058552,0.00006657003,0.00005517728,0.00004809977,0.0002490088,0.0006663078,0.004299499,0.9664542,0.02328546],"study_design_scores_gemma":[0.0009523046,0.0001791932,0.01736353,0.0004800378,0.0001255426,0.0002328581,0.00006267316,0.002303027,0.001933496,0.00429996,0.9719664,0.0001008278],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"methods","genre_scores_codex":[0.004311103,0.0007978836,0.01869627,0.006059802,0.001557775,0.001581753,0.8103408,0.04855147,0.1081031],"genre_scores_gemma":[0.01343017,0.0004544879,0.01519868,0.00504063,0.0009486738,0.001514207,0.9332275,0.004655649,0.02552998],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.1347867,"threshold_uncertainty_score":0.4509066,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02091458664898226,"score_gpt":0.2522160016506289,"score_spread":0.2313014150016466,"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."}}