{"id":"W4210749615","doi":"10.1002/pd.6108","title":"Diagnostic yield of genome sequencing for prenatal diagnosis of fetal structural anomalies","year":2022,"lang":"en","type":"article","venue":"Prenatal Diagnosis","topic":"Genomics and Rare Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"Mount Sinai Hospital; SickKids Foundation; Ontario Genomics; Hospital for Sick Children; University of Toronto","funders":"","keywords":"Prenatal diagnosis; Exome sequencing; Copy-number variation; Medicine; Exome; Medical genetics; Whole genome sequencing; Fetus; Genetic testing; Genetics; Bioinformatics; Biology; Genome; Pregnancy; Phenotype; Gene","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":[0.005839028,0.0007788088,0.0003506231,0.002329945,0.0003218066,0.000807424,0.0005560428,0.0007788382,0.003049051],"category_scores_gemma":[0.02124165,0.0002251348,0.0003682084,0.0005958987,0.0004750666,0.0004098584,0.0008230308,0.0005233438,0.0005601758],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004566039,"about_ca_system_score_gemma":0.000619288,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001764466,"about_ca_topic_score_gemma":0.00174069,"domain_scores_codex":[0.9961302,0.00160961,0.0002878195,0.00059237,0.001090484,0.0002894468],"domain_scores_gemma":[0.9915911,0.005607826,0.0007584271,0.0005591501,0.001101559,0.0003819113],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004771403,0.00004871256,0.944001,0.0000689617,0.0001053577,0.0008626163,0.00009938112,0.0008263389,0.01625149,0.0003001104,0.0008241766,0.03613466],"study_design_scores_gemma":[0.00005351787,0.0004715381,0.9363337,0.0001021211,0.0004311576,0.009955268,0.0002684095,0.01390006,0.03268292,0.001312707,0.004458481,0.00003008136],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9815695,0.003275637,0.008086515,0.0008188966,0.00007350052,0.00005150622,0.001165056,0.0001929654,0.004766391],"genre_scores_gemma":[0.9956979,0.0002930993,0.003403068,0.00006711947,0.00002986606,0.000008944663,0.0003415869,0.00001480947,0.0001435871],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005839028,"threshold_uncertainty_score":0.03088015,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01382552054380723,"score_gpt":0.2332205578956106,"score_spread":0.2193950373518033,"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."}}