{"id":"W2737370169","doi":"10.1038/gim.2017.108","title":"Knowledge base and mini-expert platform for the diagnosis of inborn errors of metabolism","year":2017,"lang":"en","type":"article","venue":"Genetics in Medicine","topic":"Genomics and Rare Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":109,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia; Genome British Columbia; BC Children's Hospital","funders":"Genome British Columbia; FP7 Health; Michael Smith Health Research BC; BC Children's Hospital; Universiteit van Amsterdam; Children's Hospital Foundation; Canadian Institutes of Health Research; Genome Canada","keywords":"Medical diagnosis; Expert system; Knowledge base; Computer science; Medicine; Genetic diagnosis; Inborn error of metabolism; Bioinformatics; Computational biology; Artificial intelligence; Pathology; Genetics; Gene; Biology; Internal medicine","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":[0.003155602,0.001226002,0.001329417,0.005464271,0.0007371177,0.00281521,0.002514755,0.002149396,0.01744552],"category_scores_gemma":[0.01256009,0.0005017173,0.001261799,0.002179926,0.0003284186,0.00272705,0.003502503,0.00124265,0.0100783],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007158005,"about_ca_system_score_gemma":0.003123672,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00416679,"about_ca_topic_score_gemma":0.004978678,"domain_scores_codex":[0.9987239,0.0002802943,0.0002206424,0.0002829187,0.0003970747,0.0000952253],"domain_scores_gemma":[0.9932798,0.003567317,0.0004071506,0.0009334901,0.001401361,0.0004108506],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0023309,0.0007336294,0.01051243,0.005172112,0.0009108605,0.007097122,0.0008387592,0.01470684,0.02542873,0.0184885,0.1960039,0.7177762],"study_design_scores_gemma":[0.0007728648,0.0005705698,0.01790129,0.002918648,0.002709297,0.007845647,0.001126836,0.2320495,0.08267784,0.1279147,0.5230417,0.0004712517],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05710642,0.01368201,0.5945075,0.01036817,0.001145513,0.002553484,0.1862551,0.08662952,0.04775219],"genre_scores_gemma":[0.2329109,0.005926176,0.5787126,0.002935089,0.0004887406,0.001113228,0.1642294,0.00168644,0.01199732],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01744552,"threshold_uncertainty_score":0.05836111,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04011651731844972,"score_gpt":0.3319995670368288,"score_spread":0.291883049718379,"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."}}