{"id":"W4407285824","doi":"10.1093/jcag/gwae059.158","title":"A158 APPLYING MACHINE LEARNING FOR PREDICTING TREATMENT RESPONSE TO VEDOLIZUMAB IN PEDIATRIC IBD BY SERUM METABOLOMICS","year":2025,"lang":"en","type":"article","venue":"Journal of the Canadian Association of Gastroenterology","topic":"Diabetes Management and Research","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Centre hospitalier universitaire Sainte-Justine; Hospital for Sick Children; Max Rady College of Medicine, University of Manitoba; University of Alberta; University of Toronto; McMaster University; Hebrew University of Jerusalem; University of Ottawa","keywords":"Vedolizumab; Metabolomics; Medicine; Machine learning; Computer science; Internal medicine; Bioinformatics; Disease; Inflammatory bowel disease; 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":[0.002194951,0.0006531977,0.0007604771,0.001405092,0.0001714679,0.0006073276,0.0003388442,0.0005388337,0.0006328256],"category_scores_gemma":[0.00419201,0.0001464154,0.0007011761,0.0006070319,0.0001418637,0.0002871121,0.0002782047,0.000460155,0.0002431575],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003874385,"about_ca_system_score_gemma":0.0005908419,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002223997,"about_ca_topic_score_gemma":0.001315453,"domain_scores_codex":[0.9991996,0.0004434868,0.0000705875,0.0001608705,0.00007047565,0.0000549539],"domain_scores_gemma":[0.9981989,0.001258597,0.0002161489,0.00007988324,0.0001798524,0.00006678455],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002453726,0.0008383626,0.5144972,0.0001865625,0.0006955389,0.0002246637,0.00005723429,0.1775488,0.01108995,0.0002631663,0.001402296,0.2907425],"study_design_scores_gemma":[0.00006682402,0.00116462,0.06079476,0.00002962548,0.0001581252,0.0001676329,0.00003546294,0.9325989,0.003961893,0.0003901262,0.000608952,0.00002298279],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9066768,0.001890764,0.08763853,0.00050968,0.00005287902,0.0001967525,0.001376082,0.0006517882,0.001006667],"genre_scores_gemma":[0.973177,0.0002408902,0.02558458,0.00006555913,0.00002734557,0.00008346789,0.0005921473,0.00001096495,0.0002178868],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002223997,"threshold_uncertainty_score":0.01160818,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009730513743456367,"score_gpt":0.2663921478856964,"score_spread":0.2566616341422401,"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."}}