{"id":"W4213135877","doi":"10.1093/jcag/gwab049.175","title":"A176 UTILITY OF MACHINE LEARNING FOR SERUM METABOLOMIC DATA ANALYSIS IN PEDIATRIC CROHN DISEASE","year":2022,"lang":"en","type":"article","venue":"Journal of the Canadian Association of Gastroenterology","topic":"Gut microbiota and health","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hospital for Sick Children; University of Alberta","funders":"","keywords":"Metabolomics; Metabolome; Disease; Machine learning; Biology; Spermidine; Bioinformatics; Medicine; Artificial intelligence; Internal medicine; Computer science; Biochemistry","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.006396428,0.00122952,0.001026621,0.003172729,0.0004149756,0.002000598,0.0006855273,0.0007853702,0.002039339],"category_scores_gemma":[0.01778247,0.0003534487,0.001131602,0.00193299,0.0003830478,0.0008683467,0.001094964,0.001216029,0.001148588],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006486214,"about_ca_system_score_gemma":0.001386739,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002724058,"about_ca_topic_score_gemma":0.002243812,"domain_scores_codex":[0.9966006,0.001880268,0.000332808,0.0006784901,0.0004157058,0.00009224653],"domain_scores_gemma":[0.99182,0.005757645,0.000660092,0.0006223216,0.0009339788,0.0002059758],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001513384,0.0006778075,0.1360526,0.000492854,0.001231425,0.0004210383,0.0002240482,0.1131874,0.0128036,0.002757325,0.007986346,0.7226522],"study_design_scores_gemma":[0.00004672877,0.0001978939,0.01691254,0.00006011479,0.00007662066,0.0002000306,0.00006402049,0.971266,0.004505891,0.004330363,0.002300315,0.0000394759],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.131384,0.001566667,0.8456114,0.001231623,0.0001741876,0.000412326,0.004208752,0.01320676,0.002204243],"genre_scores_gemma":[0.5379006,0.0004396119,0.4575887,0.0002542766,0.0001033532,0.0004894658,0.002094077,0.0003284342,0.0008015477],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006396428,"threshold_uncertainty_score":0.03382796,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01181543215234982,"score_gpt":0.2503728313391097,"score_spread":0.2385573991867599,"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."}}