{"id":"W2056717446","doi":"10.1016/j.ejmg.2012.04.005","title":"A clinical algorithm for efficient, high-resolution cytogenomic analysis of uncultured perinatal tissue samples","year":2012,"lang":"en","type":"article","venue":"European Journal of Medical Genetics","topic":"Prenatal Screening and Diagnostics","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; Mount Sinai Hospital","funders":"","keywords":"Computer science; Algorithm; Computational biology; Medicine; Biology","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":[0.003964094,0.001961794,0.001597855,0.006554406,0.001196046,0.004077592,0.003198213,0.002737029,0.006703028],"category_scores_gemma":[0.01189908,0.0008424019,0.001263767,0.002122989,0.0006847068,0.001786118,0.002154319,0.002591186,0.00522316],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001709359,"about_ca_system_score_gemma":0.002868687,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003273404,"about_ca_topic_score_gemma":0.004583048,"domain_scores_codex":[0.99766,0.0006800968,0.0004464239,0.0006001928,0.0004801927,0.0001329837],"domain_scores_gemma":[0.9960986,0.00152361,0.0003692349,0.0002856886,0.00137997,0.0003429578],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001384108,0.0005925571,0.05422489,0.0008158726,0.0002765597,0.004994538,0.0007888166,0.01757318,0.03506898,0.006313206,0.06260051,0.8153667],"study_design_scores_gemma":[0.001317844,0.001078784,0.07695973,0.001461153,0.0008251426,0.03856081,0.001707725,0.6278909,0.1011308,0.04177843,0.1066587,0.0006298841],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02048848,0.002109719,0.9501268,0.005306337,0.000261669,0.003012559,0.001839272,0.01122401,0.005631089],"genre_scores_gemma":[0.04301463,0.0005473553,0.9515395,0.000668276,0.0001149673,0.001225533,0.001550625,0.0002483736,0.001090795],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006703028,"threshold_uncertainty_score":0.0224238,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05276028857827453,"score_gpt":0.3493478882454755,"score_spread":0.2965875996672009,"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."}}