{"id":"W4391480492","doi":"10.1053/j.gastro.2024.01.033","title":"Machine Learning–Based Prediction of Pediatric Ulcerative Colitis Treatment Response Using Diagnostic Histopathology","year":2024,"lang":"en","type":"article","venue":"Gastroenterology","topic":"Inflammatory Bowel Disease","field":"Biochemistry, Genetics and Molecular Biology","cited_by":23,"is_retracted":false,"has_abstract":false,"ca_institutions":"Izaak Walton Killam Health Centre; Children's Hospital of Eastern Ontario; SickKids Foundation; University of Toronto; University of Ottawa; Hospital for Sick Children","funders":"National Institute of Diabetes and Digestive and Kidney Diseases; Cincinnati Children's Hospital Medical Center; National Institutes of Health; Crohn's and Colitis Foundation; Canadian Institutes of Health Research; University of Cincinnati; Crohn's and Colitis Foundation of America","keywords":"Histopathology; Ulcerative colitis; Medicine; Artificial intelligence; Pathology; Computer science; Disease","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":[0.001186273,0.0003966313,0.0005218616,0.001229136,0.0001741374,0.0007402002,0.0003269444,0.0004718436,0.001031838],"category_scores_gemma":[0.004890652,0.00013605,0.0005765258,0.0005361834,0.0001768124,0.0004135342,0.0003435449,0.0006127675,0.0003187693],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004267097,"about_ca_system_score_gemma":0.0003814848,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002110475,"about_ca_topic_score_gemma":0.002377565,"domain_scores_codex":[0.9995215,0.0001955925,0.00005838825,0.00008520024,0.00007943301,0.00005983648],"domain_scores_gemma":[0.997144,0.001624359,0.0005881144,0.0001050127,0.000349742,0.000188672],"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":[0.001041431,0.0001302669,0.9255025,0.00005795314,0.0001752973,0.0001992294,0.00003223831,0.01485084,0.001973013,0.0001258468,0.0009238608,0.0549876],"study_design_scores_gemma":[0.00007696685,0.0008107429,0.625565,0.00009864294,0.0004023382,0.001303499,0.0002351257,0.3628871,0.005654613,0.0008398218,0.002091733,0.00003447246],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9887233,0.001679555,0.007085094,0.0003291531,0.00004650264,0.00002931735,0.001074767,0.0001166282,0.0009155376],"genre_scores_gemma":[0.9956333,0.000236991,0.003089946,0.00002637868,0.0000279842,0.00001340547,0.0007869266,0.00000743937,0.0001775968],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002110475,"threshold_uncertainty_score":0.006273627,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01028395383275321,"score_gpt":0.24056160109399,"score_spread":0.2302776472612368,"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."}}