{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004792396,0.0002300531,0.0004229697,0.0002597457,0.00008832205,0.00000910258,0.0003756493,0.0001924357,0.0004303486],"category_scores_gemma":[0.0002792902,0.0001765536,0.00009511624,0.0004762191,0.0005747742,0.000001723007,0.0001931603,0.0001043553,0.00001908497],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000031282,"about_ca_system_score_gemma":0.00007743282,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006524388,"about_ca_topic_score_gemma":0.0005672668,"domain_scores_codex":[0.9982287,0.00008583272,0.0005043943,0.0005579706,0.0002548808,0.0003681865],"domain_scores_gemma":[0.9988092,0.00003349893,0.0001161137,0.0006651352,0.0001716956,0.0002043819],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00166656,0.000458099,0.4291186,0.0001100837,0.001438882,0.0001068404,0.002011338,0.001534324,0.4644284,0.0006924868,0.0570963,0.04133809],"study_design_scores_gemma":[0.005662634,0.00421629,0.5907415,0.0001741001,0.001479085,0.00005660812,0.001221041,0.004097711,0.03387506,0.004140776,0.3532969,0.001038293],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9875556,0.006667392,0.002586818,0.0007638147,0.0004273442,0.000193743,0.00001740289,0.00001035281,0.00177756],"genre_scores_gemma":[0.9921455,0.002328984,0.001528486,0.0009579667,0.002253927,0.00001104325,0.0003030187,0.00002828465,0.000442723],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4305534,"threshold_uncertainty_score":0.7199648,"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."}}