{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00117599,0.0006057154,0.001117148,0.001823354,0.0005700324,0.000062633,0.0002774953,0.0001640429,0.00009503829],"category_scores_gemma":[0.0005245476,0.0006417591,0.002349821,0.002667892,0.001114018,0.0002335984,0.0001622005,0.000524378,0.0005491198],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003428179,"about_ca_system_score_gemma":0.0002340097,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006819013,"about_ca_topic_score_gemma":0.000005052367,"domain_scores_codex":[0.9957093,0.0005722477,0.0008340426,0.001191964,0.0005968635,0.00109556],"domain_scores_gemma":[0.9957194,0.002303821,0.0002711671,0.001005414,0.0003355431,0.0003646023],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00317104,0.0001759813,0.9205702,0.0001287243,0.001747183,0.003600256,0.0003574926,0.002373798,0.002992493,0.0005677508,0.01277235,0.05154277],"study_design_scores_gemma":[0.001131918,0.0005830688,0.9773698,0.0001950765,0.002206818,0.001540605,0.001901921,0.008980887,0.002236111,0.0004011738,0.002558855,0.0008937511],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9454715,0.02079137,0.0189994,0.00346719,0.00543302,0.001460835,0.0004116758,0.001711696,0.002253324],"genre_scores_gemma":[0.9927788,0.0003967999,0.003537565,0.0006282458,0.0003827414,0.0001695255,0.001897407,0.0001368592,0.00007209463],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05679965,"threshold_uncertainty_score":0.9996034,"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."}}