{"id":"W2526281222","doi":"10.1016/j.cjca.2016.07.127","title":"CAN NON-CONTRAST T1 MAPPING CARDIAC MAGNETIC RESONANCE IMAGING AT 3 TESLA IDENTIFY REPLACEMENT FIBROSIS IN ISCHEMIC AND NON-ISCHEMIC CARDIOMYOPATHY? COMPARISON TO LATE GADOLINIUM ENHANCEMENT IMAGING","year":2016,"lang":"en","type":"article","venue":"Canadian Journal of Cardiology","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Calgary Laboratory Services","funders":"","keywords":"Medicine; Magnetic resonance imaging; Gadolinium; Hypertrophic cardiomyopathy; Fibrosis; Myocardial fibrosis; Cardiology; Cardiac magnetic resonance; Ischemic cardiomyopathy; Cardiac magnetic resonance imaging; Contrast (vision); Internal medicine; Pathological; Cardiac fibrosis; Heart failure; Radiology; Ejection fraction","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.004951539,0.001031853,0.001121706,0.001465576,0.0003679631,0.001943652,0.001228827,0.002455994,0.001751363],"category_scores_gemma":[0.01321357,0.0005101555,0.0005409182,0.0004881672,0.0007372751,0.003414582,0.0004400927,0.0008613556,0.0006472293],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003140754,"about_ca_system_score_gemma":0.0004173217,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001434811,"about_ca_topic_score_gemma":0.00346104,"domain_scores_codex":[0.9993761,0.0002410988,0.00007791428,0.0001003079,0.0001021941,0.0001023557],"domain_scores_gemma":[0.9930946,0.004800189,0.0005915727,0.0002900829,0.0008675428,0.0003560388],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.02821202,0.0009704067,0.5394629,0.002388299,0.001171365,0.006386223,0.001239345,0.003113723,0.1258615,0.001239283,0.002103389,0.2878515],"study_design_scores_gemma":[0.001002204,0.009181309,0.8689064,0.0007698879,0.003156912,0.02222504,0.002133764,0.03126767,0.04793212,0.004887766,0.00826723,0.0002696979],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9658378,0.01710771,0.00929573,0.001653224,0.0003237146,0.00009499102,0.0001612607,0.0001170381,0.005408411],"genre_scores_gemma":[0.9847037,0.004810111,0.00890873,0.0003514856,0.0002926576,0.00002574801,0.0002031001,0.00007365416,0.0006308591],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004951539,"threshold_uncertainty_score":0.02618653,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009682909048583462,"score_gpt":0.270812183465039,"score_spread":0.2611292744164556,"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."}}