{"id":"W2155954736","doi":"10.1161/circimaging.115.003642","title":"Iron-Sensitive Cardiac Magnetic Resonance Imaging for Prediction of Ventricular Arrhythmia Risk in Patients With Chronic Myocardial Infarction","year":2015,"lang":"en","type":"article","venue":"Circulation Cardiovascular Imaging","topic":"Cardiac Imaging and Diagnostics","field":"Medicine","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Libin Cardiovascular Institute of Alberta; London Health Sciences Centre","funders":"National Center for Advancing Translational Sciences; National Heart, Lung, and Blood Institute","keywords":"Medicine; Cardiology; Internal medicine; Myocardial infarction; Interquartile range; Ejection fraction; Magnetic resonance imaging; Cardiac magnetic resonance imaging; Implantable cardioverter-defibrillator; Sudden cardiac death; Cardiac magnetic resonance; Radiology; Heart failure","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":[0.001122955,0.0002855482,0.000756718,0.0003917131,0.00009628582,0.00003935251,0.00005207496,0.00007290183,0.000001594553],"category_scores_gemma":[0.0006841228,0.0002959011,0.0007731426,0.0005622224,0.0001351661,0.000313696,0.00003793925,0.0002519389,0.00000563226],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008344253,"about_ca_system_score_gemma":0.0002991271,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002128973,"about_ca_topic_score_gemma":0.000001353918,"domain_scores_codex":[0.9973345,0.0002357616,0.0004979342,0.000584481,0.0009249973,0.0004222968],"domain_scores_gemma":[0.9978396,0.0001234325,0.0001859987,0.0006667551,0.00100921,0.000174987],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002095744,0.00004147987,0.9070902,0.00007826986,0.0002101662,0.00003094068,0.0002036127,0.02591968,0.00003128089,0.00000986468,0.0001451093,0.06602986],"study_design_scores_gemma":[0.009499169,0.0001024437,0.9698144,0.0002844244,0.001461075,0.0000558367,0.0001311359,0.01288378,0.0001498986,0.00003388035,0.005327835,0.0002560937],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8970755,0.04943759,0.04750353,0.0001578381,0.001471227,0.003220156,0.0004154494,0.0001797,0.0005389711],"genre_scores_gemma":[0.9975196,0.00007083785,0.0008464062,0.00003517569,0.000851172,0.0001088323,0.000493774,0.00006979155,0.000004467368],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.100444,"threshold_uncertainty_score":0.9999493,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00662047401874851,"score_gpt":0.20564696074324,"score_spread":0.1990264867244915,"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."}}