{"id":"W2897528226","doi":"10.1002/humu.23643","title":"Scaling resolution of variant classification differences in ClinVar between 41 clinical laboratories through an outlier approach","year":2018,"lang":"en","type":"article","venue":"Human Mutation","topic":"Genomics and Rare Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":66,"is_retracted":false,"has_abstract":true,"ca_institutions":"Children's Hospital of Eastern Ontario","funders":"National Human Genome Research Institute","keywords":"Outlier; Genetic variants; Identification (biology); Computational biology; Computer science; Biology; Artificial intelligence; Genetics; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003483579,0.00008267858,0.0001403809,0.00003437744,0.00009023726,0.00002106931,0.00009348824,0.0001439327,0.000003436135],"category_scores_gemma":[0.00009591167,0.000077595,0.0000393432,0.00009010627,0.0001709607,0.00001447779,0.0000250446,0.00005363768,0.000001817139],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009817777,"about_ca_system_score_gemma":0.00006036673,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003472673,"about_ca_topic_score_gemma":0.00004125761,"domain_scores_codex":[0.9989606,0.0001642675,0.0003958166,0.0002803011,0.00009078593,0.0001082706],"domain_scores_gemma":[0.9993955,0.00001449057,0.0001803544,0.0002025165,0.0001705674,0.00003652626],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003558141,0.001257374,0.7673776,0.0001385645,0.0001518042,0.000005496177,0.003935007,0.0000992367,0.194229,0.02382432,0.0002351994,0.008390547],"study_design_scores_gemma":[0.0005386139,0.0004909841,0.9882089,0.0000184718,0.00003608326,0.000001184584,0.00103647,0.0008487815,0.006548958,0.00174019,0.0003773677,0.0001540221],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9920947,0.00009672489,0.007151106,0.00001551116,0.00007936705,0.000129545,0.00002547752,0.000007393206,0.0004001657],"genre_scores_gemma":[0.9964579,0.00004219773,0.002143534,0.00002699275,0.0005102537,0.00001025439,0.00078587,0.0000103298,0.0000126158],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2208312,"threshold_uncertainty_score":0.3164232,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1116306007184427,"score_gpt":0.3733427935820054,"score_spread":0.2617121928635627,"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."}}