{"id":"W2947783957","doi":"10.1186/s12968-019-0539-2","title":"Fetal XCMR: a numerical phantom for fetal cardiovascular magnetic resonance imaging","year":2019,"lang":"en","type":"article","venue":"Journal of Cardiovascular Magnetic Resonance","topic":"Fetal and Pediatric Neurological Disorders","field":"Medicine","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"SickKids Foundation; Hospital for Sick Children; University of Toronto","funders":"National Institute of Biomedical Imaging and Bioengineering; National Heart, Lung, and Blood Institute; Canadian Institutes of Health Research","keywords":"Torso; Imaging phantom; Magnetic resonance imaging; Medicine; In utero; Fetus; Coronal plane; Sagittal plane; Fetal heart; Fetal echocardiography; Radiology; Anatomy; Prenatal diagnosis; Pregnancy","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":[0.0006426182,0.0004100747,0.0002020352,0.0003738612,0.000175449,0.0006045302,0.000867628,0.0007428277,0.003115457],"category_scores_gemma":[0.002373397,0.0002164285,0.0003191817,0.0002227736,0.0003722787,0.0002836293,0.0006284143,0.0004564418,0.0005935692],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003191805,"about_ca_system_score_gemma":0.0007281871,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001191503,"about_ca_topic_score_gemma":0.001001469,"domain_scores_codex":[0.9997457,0.00008946367,0.00001695558,0.00002777538,0.0001062472,0.00001401434],"domain_scores_gemma":[0.9992836,0.0004319649,0.00008165035,0.00007581643,0.0000923446,0.00003465487],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004039782,0.0002042543,0.004214214,0.000479107,0.00005421391,0.0008885591,0.0003451292,0.7823827,0.110987,0.0178014,0.006478124,0.0757614],"study_design_scores_gemma":[0.00004663519,0.000183257,0.001635428,0.00007234707,0.0000301142,0.0008157621,0.00003728123,0.9467566,0.0305312,0.002490283,0.01734897,0.00005210034],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07068033,0.0005878208,0.9145714,0.0005326749,0.0001284207,0.0002661202,0.001190146,0.003333534,0.008709589],"genre_scores_gemma":[0.4057099,0.0008718998,0.5863895,0.0002440057,0.00004157894,0.0005708924,0.001641723,0.0006786533,0.00385171],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003115457,"threshold_uncertainty_score":0.01042223,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00886440555480484,"score_gpt":0.2230214631016586,"score_spread":0.2141570575468538,"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."}}