{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01154773,0.0007870593,0.0006343443,0.001889022,0.0005335489,0.00127711,0.0007303025,0.0009143344,0.0009151654],"category_scores_gemma":[0.03053373,0.0002515175,0.0005646276,0.0009899281,0.0004751776,0.0008666723,0.001744997,0.000834287,0.0003748623],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005454253,"about_ca_system_score_gemma":0.0009260292,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004300037,"about_ca_topic_score_gemma":0.006014168,"domain_scores_codex":[0.9934282,0.00357936,0.0004867347,0.001330724,0.0009104942,0.0002645778],"domain_scores_gemma":[0.9831278,0.01169515,0.001988822,0.001197807,0.0009168994,0.001073415],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004229248,0.00007282651,0.9758072,0.00005031261,0.0001842066,0.0002754187,0.0005735353,0.00255044,0.001299064,0.0002679022,0.000871392,0.01762474],"study_design_scores_gemma":[0.00008596538,0.0006616674,0.9292825,0.00009290164,0.0001238582,0.001712676,0.00124723,0.05811273,0.003740897,0.002002195,0.002873499,0.0000638206],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9924867,0.0002159936,0.003825333,0.0003722057,0.00001275292,0.00004393481,0.002049226,0.0001000411,0.000893811],"genre_scores_gemma":[0.9888248,0.0000772877,0.008108147,0.00009944363,0.00001635576,0.00003821089,0.002625218,0.00003690136,0.0001736736],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01154773,"threshold_uncertainty_score":0.06107098,"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."}}