{"id":"W2264251407","doi":"10.1007/s00246-015-1329-8","title":"Novel Cardiac Magnetic Resonance Feature Tracking (CMR-FT) Analysis for Detection of Myocardial Fibrosis in Pediatric Hypertrophic Cardiomyopathy","year":2016,"lang":"en","type":"article","venue":"Pediatric Cardiology","topic":"Cardiomyopathy and Myosin Studies","field":"Medicine","cited_by":53,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"National Center for Research Resources; National Center for Advancing Translational Sciences; Circle Cardiovascular Imaging; University of Utah","keywords":"Hypertrophic cardiomyopathy; Medicine; Myocardial fibrosis; Cardiology; Internal medicine; Feature tracking; Magnetic resonance imaging; Fibrosis; Cardiac magnetic resonance; Cardiomyopathy; Radiology; Heart failure","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009795754,0.0004698872,0.002543882,0.001462794,0.0001272508,0.00001277543,0.0001785695,0.0005203417,0.000004580136],"category_scores_gemma":[0.0008500146,0.0003706866,0.002327523,0.002696422,0.0001577567,0.0001153313,0.0001365106,0.0003081206,0.00000741835],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002523344,"about_ca_system_score_gemma":0.0001793721,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001055395,"about_ca_topic_score_gemma":0.00002527692,"domain_scores_codex":[0.9967363,0.000232353,0.0007831131,0.0009797432,0.0004521168,0.0008164013],"domain_scores_gemma":[0.9974253,0.0009124299,0.0002695304,0.0007414682,0.0004784824,0.0001728381],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001039905,0.0000555465,0.9526974,0.0002079759,0.0007963388,0.00005217448,0.0001433273,0.0001681022,0.02067431,0.00001811565,0.0004296183,0.02371718],"study_design_scores_gemma":[0.00362762,0.0006988068,0.9779406,0.0000178929,0.01204948,0.0001281917,0.00008097493,0.00002283103,0.0008092912,0.00003289106,0.004164014,0.0004274762],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9308637,0.0552318,0.009902626,0.0002808818,0.001535938,0.001149463,0.0005522955,0.0000996184,0.0003836481],"genre_scores_gemma":[0.9772508,0.0159618,0.0006308345,0.00004062019,0.005475675,0.0003845778,0.000030185,0.00006101815,0.0001644686],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04638709,"threshold_uncertainty_score":0.9998745,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01428494333533625,"score_gpt":0.2412103027798835,"score_spread":0.2269253594445473,"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."}}