{"id":"W2905077642","doi":"10.1038/s41436-018-0376-y","title":"Atypical cerebral palsy: genomics analysis enables precision medicine","year":2018,"lang":"en","type":"article","venue":"Genetics in Medicine","topic":"Genomics and Rare Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":60,"is_retracted":false,"has_abstract":false,"ca_institutions":"Alberta Children's Hospital; University of Calgary; University of Alberta; BC Children's Hospital; University of British Columbia","funders":"Stichting Metakids; Michael Smith Health Research BC; BC Children's Hospital; Children's Hospital Foundation; Children’s Hospital of Wisconsin Research Institute; Canadian Institutes of Health Research; Genome Canada; Genome British Columbia; National Ataxia Foundation","keywords":"Medicine; Cerebral palsy; Genetic testing; Neuroimaging; Intellectual disability; Etiology; Pediatrics; Genetic counseling; Neurology; Disease; Bioinformatics; Pathology; Psychiatry; Internal medicine; Genetics; Biology","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":[0.00289416,0.000612165,0.001091633,0.002088142,0.0004775964,0.002828413,0.0009779996,0.001702385,0.002192226],"category_scores_gemma":[0.00577591,0.0004146852,0.00061502,0.001077866,0.001571135,0.00195955,0.001508387,0.002006907,0.0006812474],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001052286,"about_ca_system_score_gemma":0.001189484,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001125476,"about_ca_topic_score_gemma":0.001073773,"domain_scores_codex":[0.9985394,0.0005335728,0.00007526193,0.0004024482,0.0003439221,0.0001054334],"domain_scores_gemma":[0.9960285,0.00234659,0.00045744,0.0006772798,0.0003392415,0.0001508524],"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":[0.001059897,0.0003040752,0.08954161,0.001087935,0.0008890076,0.003854871,0.0008217418,0.01223889,0.1783405,0.1337639,0.01748176,0.5606158],"study_design_scores_gemma":[0.0001949277,0.0005644757,0.08715458,0.000746418,0.001037971,0.01281814,0.0009111891,0.03929796,0.1108218,0.6373298,0.1088397,0.0002830423],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3044665,0.05729675,0.5330008,0.05675754,0.00162095,0.0001328817,0.003970361,0.003698788,0.03905542],"genre_scores_gemma":[0.8562525,0.01573381,0.1154428,0.007147672,0.001460986,0.00008065961,0.0007340252,0.0003403146,0.002807234],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00289416,"threshold_uncertainty_score":0.015306,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01550102035629439,"score_gpt":0.2918675918869326,"score_spread":0.2763665715306382,"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."}}