{"id":"W4291383171","doi":"10.1038/s41431-022-01171-1","title":"KBG syndrome: videoconferencing and use of artificial intelligence driven facial phenotyping in 25 new patients","year":2022,"lang":"en","type":"article","venue":"European Journal of Human Genetics","topic":"Genomics and Rare Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; University of Toronto; University Health Network; University of Alberta","funders":"National Institute of General Medical Sciences; U.S. Department of Health and Human Services; National Institutes of Health; U.S. National Library of Medicine; University of Alberta; Office for People With Developmental Disabilities","keywords":"Autism; Autism spectrum disorder; Medical genetics; Medicine; Intellectual disability; Pediatrics; Genetics; Biology; Psychiatry; Gene","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":[0.000201312,0.0001004632,0.0001479354,0.0001064355,0.00009137035,0.00002974501,0.0002089262,0.00001565937,0.00002885215],"category_scores_gemma":[0.00005026985,0.0001052386,0.00006696384,0.00005691426,0.00004670332,0.000004735709,0.0002693698,0.0001298043,7.176512e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001545376,"about_ca_system_score_gemma":0.00008658286,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005279648,"about_ca_topic_score_gemma":0.0000117611,"domain_scores_codex":[0.9989129,0.0001914104,0.0004798349,0.0001415998,0.0001432455,0.0001310602],"domain_scores_gemma":[0.9993929,0.000008557413,0.0002963679,0.0001276128,0.00008386459,0.00009067818],"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":[0.0005613231,0.0009207872,0.1302585,0.0000891951,0.000277771,0.0006893324,0.00440084,0.03709292,0.5779363,0.0003791982,0.001081169,0.2463127],"study_design_scores_gemma":[0.003559635,0.01272759,0.8947436,0.0003345994,0.0003011817,0.0005872577,0.003683281,0.001556774,0.04862975,0.002039927,0.0301724,0.001664062],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.998368,0.0005231216,0.0008156325,0.00001296198,0.0001331587,0.00007359213,0.00001842329,0.000001116225,0.00005394233],"genre_scores_gemma":[0.9989207,0.0001230893,0.0007523157,0.00004559334,0.00009240839,3.235271e-7,0.00001635429,0.00002012654,0.00002910809],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7644851,"threshold_uncertainty_score":0.4291506,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03209863909858653,"score_gpt":0.2459732967222636,"score_spread":0.2138746576236771,"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."}}