{"id":"W2905730820","doi":"10.1016/j.jcct.2018.12.002","title":"Longitudinal assessment of coronary plaque volume change related to glycemic status using serial coronary computed tomography angiography: A PARADIGM (Progression of AtheRosclerotic PlAque DetermIned by Computed TomoGraphic Angiography Imaging) substudy","year":2018,"lang":"en","type":"article","venue":"Journal of cardiovascular computed tomography","topic":"Cardiac Imaging and Diagnostics","field":"Medicine","cited_by":42,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"National Research Foundation of Korea; Ministry of Science, ICT and Future Planning","keywords":"Medicine; Glycemic; Confidence interval; Computed tomographic angiography; Odds ratio; Diabetes mellitus; Internal medicine; Computed tomography angiography; Cardiology; Angiography; Radiology; Endocrinology","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","metaepi_broad"],"consensus_categories":[],"category_scores_codex":[0.001398208,0.001189883,0.003683121,0.006356025,0.0003416547,0.000122532,0.0007804203,0.0004523724,0.00003390219],"category_scores_gemma":[0.00004948202,0.001154164,0.01128838,0.009134179,0.001313395,0.0005531939,0.0003685374,0.001007578,0.000002885757],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001617365,"about_ca_system_score_gemma":0.0003873902,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004036035,"about_ca_topic_score_gemma":0.000006762734,"domain_scores_codex":[0.9910218,0.0009293871,0.002821199,0.001179547,0.002637909,0.001410169],"domain_scores_gemma":[0.9923934,0.0003727546,0.001925178,0.001613634,0.002485358,0.001209684],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003117545,0.001766216,0.9613684,0.0003757425,0.0215742,0.0008131354,0.0005904061,0.002036118,0.002183987,0.00001412282,0.001595327,0.004564747],"study_design_scores_gemma":[0.01456818,0.005771308,0.9549344,0.002882674,0.008154324,0.002737731,0.0001498768,0.00821628,0.0006271735,0.0000771005,0.0008491941,0.001031744],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9173885,0.0449636,0.03216518,0.0001479942,0.002358862,0.002384462,0.0003548199,0.0001976603,0.0000389478],"genre_scores_gemma":[0.9740406,0.0004237041,0.02431056,0.0001694445,0.0005534211,0.00003743901,0.0002747393,0.0001892515,8.072819e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05665216,"threshold_uncertainty_score":0.9990909,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01847105170138144,"score_gpt":0.2815902365793363,"score_spread":0.2631191848779548,"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."}}