{"id":"W4409147590","doi":"10.1002/ajmg.a.64067","title":"Human Phenotype Ontology Annotations for Rare Congenital Conditions: Application to Arthrogryposis Multiplex Congenita","year":2025,"lang":"en","type":"article","venue":"American Journal of Medical Genetics Part A","topic":"Genomics and Rare Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Children's Hospital; McGill University Health Centre; Alberta Health Services; McGill University; Shriners Hospitals for Children - Canada","funders":"Fonds de Recherche du Québec - Santé; National Human Genome Research Institute; Shriners Hospitals for Children; American Society for Bone and Mineral Research","keywords":"Arthrogryposis multiplex congenita; Arthrogryposis; Phenotype; Ontology; Protocol (science); Encoding (memory); Computer science; Computational biology; Medicine; Biology; Genetics; Artificial intelligence; Pathology; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004592436,0.0007688346,0.0005376685,0.006932274,0.001358844,0.001620257,0.0008971034,0.0007743192,0.005306046],"category_scores_gemma":[0.02283425,0.0003250234,0.001483952,0.004790634,0.0004557831,0.001823457,0.00321454,0.001248267,0.001446155],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001503235,"about_ca_system_score_gemma":0.005400035,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01378442,"about_ca_topic_score_gemma":0.0224333,"domain_scores_codex":[0.9973744,0.0007971977,0.000485215,0.0005632298,0.0006639457,0.0001160436],"domain_scores_gemma":[0.9894372,0.005646031,0.00120376,0.001436144,0.001855329,0.0004214351],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0009612642,0.0004507925,0.1138356,0.01235555,0.001075529,0.00567801,0.008734412,0.01055624,0.04723924,0.04070511,0.1337901,0.6246182],"study_design_scores_gemma":[0.0002074914,0.0002631186,0.1417222,0.004061304,0.001122704,0.006810104,0.004693992,0.03279498,0.01889942,0.05149641,0.7376261,0.0003021628],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1411589,0.006891266,0.5427068,0.006515164,0.001203211,0.002351635,0.2510324,0.01873494,0.02940551],"genre_scores_gemma":[0.1727758,0.003019829,0.6598191,0.001209895,0.0001334925,0.001277413,0.1563503,0.002019384,0.003394867],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01378442,"threshold_uncertainty_score":0.02740836,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00916032048794998,"score_gpt":0.3250440824202716,"score_spread":0.3158837619323216,"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."}}