{"id":"W2031550416","doi":"10.1016/j.jcct.2010.08.006","title":"What is the optimal number of readers needed to achieve high diagnostic accuracy in coronary computed tomographic angiography? A comparison of alternate reader combinations","year":2010,"lang":"en","type":"article","venue":"Journal of cardiovascular computed tomography","topic":"Cardiac Imaging and Diagnostics","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Medicine; Computed tomographic angiography; Computed tomographic; Coronary angiography; Radiology; Angiography; Diagnostic accuracy; Computed tomography; Cardiology; 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":[0.03426071,0.002311419,0.006269171,0.003366307,0.001202529,0.003778281,0.002303089,0.004685582,0.004045816],"category_scores_gemma":[0.1317185,0.002288489,0.002116101,0.002177558,0.001146264,0.006939831,0.001936828,0.002289442,0.002811911],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005852615,"about_ca_system_score_gemma":0.001015124,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002011764,"about_ca_topic_score_gemma":0.0004471673,"domain_scores_codex":[0.9545569,0.02903345,0.006017501,0.003588827,0.005706794,0.001096442],"domain_scores_gemma":[0.8385766,0.1292666,0.007943887,0.00881336,0.01249662,0.002902809],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.06442183,0.003761628,0.1840707,0.003058641,0.002814664,0.001606625,0.001126714,0.01520767,0.0364425,0.001847483,0.005406553,0.680235],"study_design_scores_gemma":[0.01663382,0.08838155,0.440161,0.003038904,0.02166411,0.04603826,0.003084244,0.1986234,0.1335585,0.02276499,0.02394393,0.002107238],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7317545,0.02021114,0.225633,0.00361527,0.001173629,0.001106992,0.0009378839,0.004029067,0.01153855],"genre_scores_gemma":[0.7590089,0.003092209,0.2335902,0.000842217,0.0008753191,0.0006183442,0.000577901,0.0007152849,0.0006796087],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03426071,"threshold_uncertainty_score":0.1811901,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01242823908424149,"score_gpt":0.2820542679939061,"score_spread":0.2696260289096646,"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."}}