{"id":"W1581517109","doi":"10.1016/j.jacc.2013.04.064","title":"Optimized Prognostic Score for Coronary Computed Tomographic Angiography","year":2013,"lang":"en","type":"article","venue":"Journal of the American College of Cardiology","topic":"Cardiac Imaging and Diagnostics","field":"Medicine","cited_by":272,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia; University of Ottawa","funders":"GE Healthcare; Siemens; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung","keywords":"Medicine; Computed tomographic angiography; Computed tomographic; Coronary angiography; Radiology; Angiography; Computed tomography; Cardiology; 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":[0.0007454189,0.000893641,0.001002583,0.001643191,0.0002772279,0.0009613526,0.0004838576,0.0006574841,0.00300257],"category_scores_gemma":[0.003902098,0.0001802504,0.0007379284,0.0006623574,0.000153955,0.0004410084,0.0006080412,0.0005177455,0.0008801608],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004630993,"about_ca_system_score_gemma":0.000809295,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001280276,"about_ca_topic_score_gemma":0.001575524,"domain_scores_codex":[0.9994434,0.0001322502,0.00006411637,0.0001065147,0.0001620562,0.00009178169],"domain_scores_gemma":[0.9990716,0.0002548293,0.0001886671,0.00007397991,0.0002529451,0.000158003],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002054741,0.0002037881,0.9248403,0.00005357686,0.0004009304,0.0004731552,0.00002109562,0.006109149,0.001879808,0.0005370724,0.003923745,0.05950262],"study_design_scores_gemma":[0.0003481385,0.001010416,0.8797563,0.00004875969,0.0009782836,0.001918639,0.00007662663,0.1088794,0.001535989,0.00290571,0.002467765,0.0000738011],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.9671725,0.001290869,0.02082572,0.0006344991,0.0002319537,0.0002212064,0.004818647,0.0003570767,0.004447485],"genre_scores_gemma":[0.9884638,0.0002165604,0.006731223,0.00008449812,0.0001992538,0.00009084,0.003417114,0.00002572622,0.0007709712],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.00300257,"threshold_uncertainty_score":0.01004463,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01215707152533905,"score_gpt":0.2545309498090014,"score_spread":0.2423738782836624,"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."}}