{"id":"W2191200832","doi":"10.1002/jmri.25106","title":"Comprehensive motion‐compensated highly accelerated 4D flow MRI with ferumoxytol enhancement for pediatric congenital heart disease","year":2015,"lang":"en","type":"article","venue":"Journal of Magnetic Resonance Imaging","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":123,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University Health Network","funders":"GE Healthcare; National Institutes of Health; National Center for Advancing Translational Sciences; National Institute of Biomedical Imaging and Bioengineering; American Heart Association","keywords":"Ferumoxytol; Medicine; Image quality; Steady-state free precession imaging; Nuclear medicine; Flip angle; Motion compensation; Radiology; Magnetic resonance imaging; Computer science; Artificial intelligence","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":[0.001209625,0.0003746908,0.0002375845,0.0006591474,0.0001102411,0.0002170774,0.0001968957,0.0003947963,0.000539963],"category_scores_gemma":[0.002408047,0.0002110671,0.000132564,0.0001543051,0.0003195654,0.0003201777,0.0002567351,0.0001736955,0.0001236142],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000180191,"about_ca_system_score_gemma":0.0003397691,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004689859,"about_ca_topic_score_gemma":0.0008899397,"domain_scores_codex":[0.9997748,0.00008628299,0.00002304098,0.00004336873,0.00005059957,0.00002198667],"domain_scores_gemma":[0.9993896,0.0002487754,0.0001469012,0.00005395967,0.00008363154,0.00007712092],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002582859,0.0004763202,0.4852116,0.0002682022,0.00007570058,0.004759451,0.0003911107,0.002806412,0.3544548,0.0001677763,0.0002855831,0.1485202],"study_design_scores_gemma":[0.0002552089,0.007792053,0.8396184,0.0000744752,0.000168136,0.02933819,0.0002361746,0.02035556,0.1003819,0.0001483451,0.001571239,0.00006029714],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9941561,0.0003313429,0.005247427,0.00002434057,0.000003433842,0.00003278322,0.00001771015,0.00002167653,0.0001651096],"genre_scores_gemma":[0.9821748,0.000274361,0.0173313,0.00002441285,0.00001242053,0.00002835274,0.00004850006,0.000008003422,0.00009787328],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001209625,"threshold_uncertainty_score":0.006397247,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03212150491339492,"score_gpt":0.3097836614619787,"score_spread":0.2776621565485837,"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."}}