{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005139828,0.0001670967,0.0005072745,0.0002089786,0.0000652641,0.000005303874,0.0001350543,0.0001161771,0.0002536792],"category_scores_gemma":[0.0007849619,0.0001320478,0.00008128404,0.0006615484,0.0002014928,0.00003713964,0.00003211243,0.0004015804,0.000007041282],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000347672,"about_ca_system_score_gemma":0.00004570189,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006158972,"about_ca_topic_score_gemma":0.00001119845,"domain_scores_codex":[0.9986272,0.00001514694,0.0004245718,0.0003529026,0.0002509894,0.0003291516],"domain_scores_gemma":[0.99889,0.0003143182,0.00008528474,0.0004595331,0.0001206095,0.0001302114],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003091636,0.0001890929,0.007753883,0.0001261291,0.00000562617,0.00002477299,0.0002762793,0.00005150785,0.05291227,0.005824242,0.01468533,0.9178417],"study_design_scores_gemma":[0.008169089,0.00157143,0.04387102,0.0004664052,0.0001539169,0.00006125431,0.0003365683,0.01783813,0.005410052,0.0048694,0.9168518,0.0004008923],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2787953,0.06037726,0.4968958,0.06401317,0.002437605,0.01717126,0.0001095935,0.0012108,0.0789893],"genre_scores_gemma":[0.1595272,0.001645892,0.8309914,0.0009337145,0.001007196,0.00150963,0.00005926922,0.0000640335,0.004261671],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9174408,"threshold_uncertainty_score":0.5384752,"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."}}