{"id":"W2111422238","doi":"10.1002/mrm.22542","title":"Metric optimized gating for fetal cardiac MRI","year":2010,"lang":"en","type":"article","venue":"Magnetic Resonance in Medicine","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":110,"is_retracted":false,"has_abstract":true,"ca_institutions":"Children's Hospital of Eastern Ontario; SickKids Foundation; Hospital for Sick Children; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Gating; Imaging phantom; Magnetic resonance imaging; Pulsatile flow; Metric (unit); Computer science; SIGNAL (programming language); Population; Biomedical engineering; Pulse (music); Artificial intelligence; Physics; Medicine; Radiology; Engineering; Optics; Cardiology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0006540739,0.0004181365,0.0002869873,0.0003220896,0.0001223377,0.0004086636,0.00034889,0.0003228277,0.0007792101],"category_scores_gemma":[0.003586277,0.0002197222,0.0001815368,0.0003939203,0.0002401347,0.0003351849,0.0004120348,0.0004053529,0.0001911732],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003764435,"about_ca_system_score_gemma":0.000506044,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008385901,"about_ca_topic_score_gemma":0.001157421,"domain_scores_codex":[0.9996525,0.0001612888,0.00001562583,0.00002721687,0.0001238554,0.00001956066],"domain_scores_gemma":[0.9993981,0.0003252918,0.00008011051,0.00008074791,0.00009221049,0.00002352062],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004112724,0.00007423742,0.003364722,0.0001752606,0.00005365848,0.000353889,0.0001562356,0.4579571,0.16007,0.03067901,0.002249407,0.3444551],"study_design_scores_gemma":[0.0000145184,0.0001295196,0.001672787,0.00001197495,0.00001093424,0.0002975383,0.000008130618,0.9579038,0.02990942,0.006055041,0.003956879,0.000029484],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02008029,0.0002632274,0.9785363,0.00005104425,0.00001746093,0.00003190039,0.00004518347,0.0003233007,0.0006513653],"genre_scores_gemma":[0.3549327,0.0003884611,0.6434638,0.00006128648,0.00002532589,0.00008433958,0.0002106005,0.0001887191,0.0006447456],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0008385901,"threshold_uncertainty_score":0.003459096,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01670769233142799,"score_gpt":0.3342308998254632,"score_spread":0.3175232074940352,"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."}}