{"id":"W2025089841","doi":"10.1016/j.cjca.2012.11.004","title":"Multislice Computed Tomographic Coronary Angiography for Quantitative Assessment of Culprit Lesions in Acute Coronary Syndromes","year":2013,"lang":"en","type":"article","venue":"Canadian Journal of Cardiology","topic":"Cardiac Imaging and Diagnostics","field":"Medicine","cited_by":55,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Medicine; Acute coronary syndrome; Culprit; Hounsfield scale; Multislice computed tomography; Multislice; Vulnerable plaque; Calcification; Cardiology; Myocardial infarction; Unstable angina; Internal medicine; Radiology; Fibrous cap; Nuclear medicine; Computed tomography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.001838776,0.0006245741,0.0005899146,0.001998309,0.0002944567,0.0008730553,0.0006242373,0.0006916515,0.001126887],"category_scores_gemma":[0.005291092,0.0004175647,0.0002319034,0.0007530148,0.0002984554,0.0007548544,0.0003551482,0.0007712208,0.0002639254],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002482379,"about_ca_system_score_gemma":0.0005077975,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001600292,"about_ca_topic_score_gemma":0.005812176,"domain_scores_codex":[0.9994833,0.0002010087,0.00008450739,0.00003893522,0.0001473323,0.00004483871],"domain_scores_gemma":[0.9980447,0.0009612757,0.0002306376,0.0001318881,0.0003878647,0.0002435787],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.004168926,0.0004969888,0.8679062,0.0002930466,0.0002367598,0.004504188,0.0001922103,0.0008136667,0.04260184,0.0004789512,0.001540237,0.07676687],"study_design_scores_gemma":[0.0002567072,0.001473593,0.9444932,0.0001809376,0.0003706019,0.01536165,0.000316937,0.02123345,0.01363071,0.0005347825,0.002080226,0.00006716135],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9779154,0.008604235,0.007001831,0.0004682993,0.0001127634,0.0002182616,0.0003980026,0.0001258843,0.005155313],"genre_scores_gemma":[0.9867436,0.001465454,0.0108659,0.0001339342,0.000113822,0.00006233172,0.0001595541,0.00002100729,0.0004343659],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001998309,"threshold_uncertainty_score":0.009724498,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02662900876657947,"score_gpt":0.3131415452510085,"score_spread":0.286512536484429,"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."}}