{"id":"W1989751838","doi":"10.1016/s0735-1097(10)60711-5","title":"THE POTENTIAL OF 64-SLICE MULTIDETECTOR COMPUTED TOMOGRAPHY CORONARY ANGIOGRAPHY TO REPLACE INVASIVE CORONARY ANGIOGRAPHY - THE ONTARIO MULTIDETECTOR COMPUTED TOMOGRAPHY CORONARY ANGIOGRAPHY STUDY (OMCAS)","year":2010,"lang":"en","type":"article","venue":"Journal of the American College of Cardiology","topic":"Cardiac Imaging and Diagnostics","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Medicine; Coronary angiography; Multidetector computed tomography; Radiology; Computed tomography; Angiography; Computed tomography angiography; Tomography; Cardiology; Myocardial infarction","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00178,0.0004008347,0.0003840317,0.0008765532,0.0003403767,0.00102393,0.000774289,0.001023346,0.001835703],"category_scores_gemma":[0.00726771,0.0002177339,0.0003751951,0.0009534586,0.0006966793,0.0007733952,0.0003058037,0.0006082991,0.0005002455],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00132899,"about_ca_system_score_gemma":0.004326867,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1010703,"about_ca_topic_score_gemma":0.2936843,"domain_scores_codex":[0.9995521,0.00009019474,0.00005125701,0.00005520855,0.0002021042,0.00004921513],"domain_scores_gemma":[0.9954463,0.001177252,0.0006070146,0.0006042263,0.001648189,0.000517076],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001337277,0.0001341288,0.7597175,0.0002464101,0.0001563502,0.001293721,0.0001476711,0.0005622102,0.005915252,0.001778449,0.01194828,0.2167626],"study_design_scores_gemma":[0.0002492318,0.0004155072,0.9266503,0.0002079205,0.0002927887,0.00535824,0.0002489282,0.002853238,0.001834018,0.002117494,0.05969634,0.0000759906],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.869209,0.04274742,0.006919948,0.02833479,0.002183247,0.0002785001,0.004580876,0.0002400908,0.04550615],"genre_scores_gemma":[0.9594817,0.01402245,0.01553643,0.002917346,0.001324909,0.00004349875,0.002083277,0.00006178882,0.004528653],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1010703,"threshold_uncertainty_score":0.200964,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007276498172508625,"score_gpt":0.2398648483601229,"score_spread":0.2325883501876143,"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."}}