{"id":"W4205511467","doi":"10.1016/j.cjca.2022.01.011","title":"Management of Patients With Single-Ventricle Physiology Across the Lifespan: Contributions From Magnetic Resonance and Computed Tomography Imaging","year":2022,"lang":"en","type":"article","venue":"Canadian Journal of Cardiology","topic":"Congenital Heart Disease Studies","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"University Health Network; Hospital for Sick Children","funders":"","keywords":"Magnetic resonance imaging; Cardiac imaging; Risk stratification; Cardiac magnetic resonance imaging; Modalities; Population; Computed tomography; Clinical Practice; Functional imaging","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":[0.0005848313,0.0002685113,0.0003808609,0.0008497998,0.0004878615,0.0008126008,0.000354153,0.0005869237,0.0005699516],"category_scores_gemma":[0.004133717,0.00008992948,0.0002410153,0.0005915858,0.0002551691,0.0009562652,0.0004922018,0.000687782,0.00008222361],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006083771,"about_ca_system_score_gemma":0.0009186298,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005145175,"about_ca_topic_score_gemma":0.01452256,"domain_scores_codex":[0.9997134,0.00006648502,0.00004438485,0.00005045384,0.0000793586,0.00004599863],"domain_scores_gemma":[0.9991849,0.0001895709,0.0002577058,0.00002829371,0.0001358815,0.0002035555],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001001928,0.0001850565,0.8943676,0.00009937031,0.000107265,0.006146991,0.0006204405,0.0003853178,0.0009861643,0.0004858801,0.00256912,0.0939467],"study_design_scores_gemma":[0.00001581768,0.0002496235,0.9743308,0.0006242317,0.00009380945,0.01452456,0.003358278,0.0009402069,0.0002600307,0.001567649,0.004002141,0.00003292032],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9251155,0.04617454,0.001194029,0.01407169,0.0003440892,0.00003123856,0.000315584,0.00002340781,0.01272986],"genre_scores_gemma":[0.9786894,0.01784633,0.001418097,0.0009516829,0.0004525456,0.00001890701,0.0002131189,0.000008352627,0.0004016138],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005145175,"threshold_uncertainty_score":0.01023042,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005798482851413696,"score_gpt":0.2178331138630477,"score_spread":0.212034631011634,"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."}}