{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003407898,0.0001366548,0.000606368,0.0001921023,0.00004520745,0.00001028493,0.0002867556,0.0001015727,0.0001111988],"category_scores_gemma":[0.002766394,0.0001007952,0.0003796983,0.0002950874,0.0001970984,0.00002143569,0.0001078733,0.0003427755,0.000009316759],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003181848,"about_ca_system_score_gemma":0.0001288396,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006507302,"about_ca_topic_score_gemma":0.000001133304,"domain_scores_codex":[0.9972996,0.0003406247,0.00105523,0.0001364726,0.0008760577,0.0002920896],"domain_scores_gemma":[0.9977698,0.0005295394,0.0004568917,0.0002078527,0.0002911002,0.0007447823],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0004902716,0.001281907,0.04386942,0.00009587427,0.002839606,0.0002954799,0.0005934165,0.001559841,0.001250593,0.0001788716,0.003693271,0.9438515],"study_design_scores_gemma":[0.009341793,0.004192778,0.8495748,0.0004911531,0.00938844,0.0006248442,0.0003016019,0.04147839,0.003055522,0.00001291187,0.08115705,0.0003806947],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6367497,0.004479525,0.3574371,0.0003600988,0.0006047016,0.0001276151,0.0001034281,0.00001124121,0.0001265702],"genre_scores_gemma":[0.898445,0.0005991291,0.09908774,0.0001653831,0.00158922,5.62462e-7,0.00005262957,0.00002351527,0.00003679665],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9434708,"threshold_uncertainty_score":0.4110307,"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."}}