{"id":"W4392616540","doi":"10.1016/j.gimo.2024.101046","title":"P149: Exome sequencing vs chromosomal microarray for copy number variant detection*","year":2024,"lang":"en","type":"article","venue":"Genetics in Medicine Open","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of North Carolina at Chapel Hill; School of Medicine, Stanford University; Universiteit van Amsterdam; University of Washington; Imperial College London; University of Ottawa; Universiteit Gent; Johns Hopkins University; Universitair Ziekenhuis Gent; Northwestern University; School of Medicine, Duke University; Ohio State University; Feinberg School of Medicine; University of New South Wales; University of Pennsylvania","keywords":"Exome sequencing; Copy-number variation; Biology; Genetics; Microarray; Exome; Computational biology; DNA sequencing; Microarray analysis techniques; Gene; Phenotype; Genome; Gene expression","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002803948,0.0005169163,0.0006926195,0.001167437,0.0001921115,0.001342976,0.0005439023,0.001089535,0.009992081],"category_scores_gemma":[0.006104753,0.0001719763,0.000387567,0.001068426,0.0004065056,0.0008803298,0.0005972704,0.0007667369,0.002732188],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004030178,"about_ca_system_score_gemma":0.0002843495,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007739426,"about_ca_topic_score_gemma":0.001292272,"domain_scores_codex":[0.9985313,0.0005127504,0.0001217297,0.0003993957,0.0003465374,0.00008824023],"domain_scores_gemma":[0.9975246,0.001626892,0.000211273,0.0001761826,0.0003339009,0.0001271755],"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":[0.00676982,0.0002197006,0.07817581,0.001377923,0.0005242233,0.001660433,0.0001241005,0.001474716,0.07839847,0.006225422,0.03692863,0.7881208],"study_design_scores_gemma":[0.001623909,0.006896378,0.3678688,0.002773733,0.001636974,0.05625333,0.0006704609,0.04901135,0.1823368,0.03564649,0.2948384,0.0004433331],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5428583,0.08392129,0.2191043,0.04160853,0.005998633,0.001310682,0.01417143,0.003611921,0.08741494],"genre_scores_gemma":[0.8323393,0.01811082,0.1134524,0.01234571,0.001816254,0.0007548583,0.00466243,0.0006983188,0.01581983],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009992081,"threshold_uncertainty_score":0.03342688,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0239767929501714,"score_gpt":0.3240231880602467,"score_spread":0.3000463951100752,"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."}}