{"id":"W4388549641","doi":"10.1148/ryct.230124","title":"Artificial Intelligence–based Coronary Stenosis Quantification at Coronary CT Angiography versus Quantitative Coronary Angiography","year":2023,"lang":"en","type":"article","venue":"Radiology Cardiothoracic Imaging","topic":"Cardiac Imaging and Diagnostics","field":"Medicine","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Paul's Hospital; University of British Columbia","funders":"","keywords":"Medicine; Stenosis; Coronary angiography; Receiver operating characteristic; Angiography; Radiology; Predictive value; Cardiology; Predictive value of tests; Internal medicine; 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":[0.01810427,0.0008627126,0.001015648,0.001606044,0.0001794927,0.001200815,0.000543602,0.0008201207,0.0005274095],"category_scores_gemma":[0.03473616,0.00022368,0.0007478523,0.0006938131,0.001094024,0.0008856234,0.0006182212,0.0005800928,0.0001524744],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004512222,"about_ca_system_score_gemma":0.0003398316,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005944819,"about_ca_topic_score_gemma":0.0005037664,"domain_scores_codex":[0.9862678,0.009624441,0.0006836261,0.00120018,0.002058525,0.0001653107],"domain_scores_gemma":[0.9617563,0.02849413,0.004915755,0.001893501,0.002317225,0.0006231142],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.04974901,0.002001841,0.6442977,0.001439801,0.005989522,0.0001473162,0.0005580653,0.02430713,0.02161539,0.001940588,0.001671752,0.2462818],"study_design_scores_gemma":[0.002288778,0.03408852,0.7530367,0.000257957,0.003350025,0.0007367302,0.0002851443,0.1861376,0.01226917,0.004039361,0.003230269,0.0002797926],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.966372,0.004698857,0.02389242,0.0005283708,0.0001617427,0.0002574756,0.0002968844,0.0001930328,0.003599185],"genre_scores_gemma":[0.9903245,0.0003157996,0.008609853,0.0001939366,0.0001157208,0.00008211956,0.000181244,0.000009631062,0.0001672123],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01810427,"threshold_uncertainty_score":0.09574568,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0537825152064544,"score_gpt":0.3431578678730105,"score_spread":0.2893753526665561,"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."}}