{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008237455,0.0003078332,0.0003354788,0.0006549007,0.0001489235,0.0003433174,0.000217381,0.0003358142,0.0004901388],"category_scores_gemma":[0.002052354,0.0001126864,0.0001857987,0.0002574364,0.0001712928,0.000183911,0.0002479609,0.0003149192,0.0001204488],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000135434,"about_ca_system_score_gemma":0.0001861496,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004105772,"about_ca_topic_score_gemma":0.0007476727,"domain_scores_codex":[0.9998116,0.00007728286,0.00001951666,0.00003220883,0.00003737426,0.00002200739],"domain_scores_gemma":[0.9987491,0.0003448532,0.0004429038,0.00006566737,0.0001435092,0.0002539391],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005684575,0.00004215473,0.9967188,0.000005470573,0.00002374376,0.00005421076,0.00001199681,0.00009653501,0.0007109073,0.00000920294,0.00005031307,0.001708292],"study_design_scores_gemma":[0.0000437044,0.0008200334,0.9945498,0.00001095846,0.00006775736,0.0005180415,0.00003852716,0.003313084,0.0003916673,0.00005062451,0.0001907279,0.000005005316],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9990796,0.000370026,0.0001923129,0.00003493818,0.000005437111,0.00001025801,0.00004792341,0.000004418283,0.0002551596],"genre_scores_gemma":[0.9994886,0.00007610163,0.0002763758,0.00001286377,0.00001537324,0.000005841543,0.00009453413,7.069834e-7,0.00002956782],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0008237455,"threshold_uncertainty_score":0.004356444,"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."}}