{"id":"W2151774404","doi":"10.1007/s00330-012-2652-6","title":"Fully automated derivation of coronary artery calcium scores and cardiovascular risk assessment from contrast medium-enhanced coronary CT angiography studies","year":2012,"lang":"en","type":"article","venue":"European Radiology","topic":"Cardiac Imaging and Diagnostics","field":"Medicine","cited_by":21,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; Sunnybrook Health Science Centre","funders":"","keywords":"Medicine; Agatston score; Coronary Calcium Score; Neuroradiology; Calcium; Coronary artery calcium; Radiology; Contrast (vision); Percentile; Angiography; Interventional radiology; Internal medicine; Computed tomography; Artificial intelligence","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":[0.001202374,0.0008735008,0.001113546,0.002251014,0.0002692149,0.001631882,0.0008983694,0.001083015,0.001983586],"category_scores_gemma":[0.005423794,0.0005128982,0.0008765127,0.0008919226,0.0001919755,0.0005644636,0.001058277,0.0005542052,0.002058815],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002071585,"about_ca_system_score_gemma":0.000834957,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003676881,"about_ca_topic_score_gemma":0.006181358,"domain_scores_codex":[0.9992892,0.0001523089,0.00007767789,0.0001888796,0.0002121544,0.0000798181],"domain_scores_gemma":[0.9981894,0.0006791403,0.0001416515,0.0002933489,0.0006262593,0.00007026903],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001976994,0.0003411864,0.07344115,0.0003364203,0.000607981,0.0009681907,0.0001252905,0.01684617,0.07156717,0.0005890979,0.007075525,0.8261248],"study_design_scores_gemma":[0.00024012,0.000398497,0.260557,0.000122936,0.0006031475,0.005152314,0.0001154034,0.6618786,0.05989641,0.003785345,0.007102282,0.0001479296],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3956783,0.003004354,0.5753009,0.000322127,0.0001941659,0.0003320524,0.00624772,0.01570903,0.003211386],"genre_scores_gemma":[0.7302306,0.0007221317,0.2581386,0.0001627131,0.0001837791,0.0001729789,0.007764557,0.0005039105,0.002120828],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003676881,"threshold_uncertainty_score":0.007310987,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02164621524822229,"score_gpt":0.2867474061380306,"score_spread":0.2651011908898083,"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."}}