{"id":"W2968291388","doi":"10.1016/j.carrev.2019.08.010","title":"Diagnostic Accuracy of 320-Row Computed Tomography for Characterizing Coronary Atherosclerotic Plaques: Comparison with Intravascular Optical Coherence Tomography","year":2019,"lang":"en","type":"article","venue":"Cardiovascular revascularization medicine","topic":"Coronary Interventions and Diagnostics","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University; London Health Sciences Centre","funders":"Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Medicine; Optical coherence tomography; Atheroma; Calcification; Vulnerable plaque; Fibrous cap; Intravascular ultrasound; Coronary atherosclerosis; Radiology; Lumen (anatomy); Coronary artery disease; Neovascularization; Internal medicine; Angiogenesis","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":[0.001106899,0.0005458185,0.002044827,0.000539944,0.0001263439,0.00003500683,0.0003276185,0.0002686652,0.0002548431],"category_scores_gemma":[0.001538899,0.0004403877,0.00180486,0.001242204,0.0004654205,0.0002329727,0.00009068939,0.0003836127,0.00002333534],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008382947,"about_ca_system_score_gemma":0.0001451732,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004554411,"about_ca_topic_score_gemma":0.000004637637,"domain_scores_codex":[0.9958578,0.0002242434,0.001194476,0.000893009,0.001289517,0.0005409245],"domain_scores_gemma":[0.9952495,0.00129773,0.0004041198,0.001605215,0.001087049,0.0003564212],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001513177,0.002299293,0.8846043,0.009357302,0.02086441,0.0002490492,0.00118,0.006259918,0.0160689,0.002440925,0.0009991137,0.05416366],"study_design_scores_gemma":[0.02158874,0.006594715,0.9239691,0.01116454,0.009895761,0.0006007703,0.0007920384,0.006063457,0.006334343,0.0001160354,0.01184789,0.001032549],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7135317,0.01634466,0.2624869,0.0003950619,0.0007375501,0.005981925,0.00004351839,0.0002005327,0.0002781272],"genre_scores_gemma":[0.9905263,0.0007519599,0.006468263,0.0003142978,0.000316859,0.0002449247,0.001217171,0.0001109331,0.0000492997],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2769946,"threshold_uncertainty_score":0.9998048,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01968872260420076,"score_gpt":0.2632891300623518,"score_spread":0.243600407458151,"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."}}