{"id":"W2963860045","doi":"10.1002/humu.23874","title":"CAGI SickKids challenges: Assessment of phenotype and variant predictions derived from clinical and genomic data of children with undiagnosed diseases","year":2019,"lang":"en","type":"article","venue":"Human Mutation","topic":"Genomics and Rare Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hospital for Sick Children","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Institute of General Medical Sciences; National Human Genome Research Institute; U.S. National Library of Medicine; National Institute on Aging; Eesti Teadusagentuur; National Institutes of Health; Hospital for Sick Children; Foundation for the National Institutes of Health","keywords":"Library science; Biology; Medicine; Gerontology; Family medicine; Computer science","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00006231316,0.00007199269,0.0001288973,0.00001989556,0.00003203156,0.000008401968,0.00008552543,0.00004614494,0.00001189739],"category_scores_gemma":[0.00001416004,0.00006276993,0.00001803141,0.00001326482,0.0001002623,0.000006033965,0.00009955458,0.00002842218,3.374047e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000002875904,"about_ca_system_score_gemma":0.00007409559,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008098142,"about_ca_topic_score_gemma":0.00008460791,"domain_scores_codex":[0.9993933,0.00004486201,0.0001754986,0.0002715771,0.00005720846,0.00005756289],"domain_scores_gemma":[0.9994594,0.00002238835,0.0001174934,0.0003112296,0.00004029758,0.0000492163],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001304273,0.0004048121,0.9371496,0.00007660752,0.0005891698,0.000003172158,0.0001757705,0.0002087996,0.05798741,0.0005257774,0.00004244318,0.002706023],"study_design_scores_gemma":[0.001011451,0.0003979889,0.997473,0.00001729429,0.0001556655,0.000002454984,0.0001372079,0.0003589123,0.0002115021,0.0001423986,0.00001843317,0.0000736681],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.997117,0.001637288,0.0003100501,0.00001774965,0.00002816,0.0002118912,0.0006271831,0.000003515806,0.00004719335],"genre_scores_gemma":[0.9961039,0.0008362214,0.0005039122,0.00001443863,0.0000616063,0.000004430281,0.002460732,0.00001012246,0.000004578319],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06032344,"threshold_uncertainty_score":0.2559684,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02776169988011755,"score_gpt":0.3116987136412543,"score_spread":0.2839370137611367,"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."}}