Urinary NMR metabolomic profiles discriminate inflammatory bowel disease from healthy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND AND AIMS: Inflammatory bowel disease, a chronic inflammation of the intestinal tract, presents in two variations, Ulcerative Colitis (UC) and Crohn's disease (CD). Given that treatment of CD differs from UC, a single test that provided strong diagnostic ability would offer great clinical value. Two previous studies have indicated that CD can be distinguished from UC, and that both can be distinguished from non-IBD-type gastrointestinal disease, based on urinary and faecal metabolite profiling. METHODS: Analysis of healthy as well as CD and UC patients attending an IBD clinic was performed. IBD patients were classified into two groups (CD or UC) based on chart review of clinical, endoscopic, and histological assessment. Urine samples were obtained and analyzed using nuclear magnetic resonance (NMR) spectroscopy combined with targeted profiling techniques, followed by univariate and multivariate statistical analysis. RESULTS: Based on urinary metabolomics, individuals with IBD could be differentiated from healthy. Major differences between IBD and healthy included TCA cycle intermediates, amino acids, and gut microflora metabolites. Comparison of CD and UC patients revealed discrimination, but removal of patients with the surgical intervention confounder revealed that CD could not be discriminated from UC. CONCLUSIONS: This study highlights the potential for metabolomics to distinguish IBD from the healthy state but shows that careful consideration must be given to establishing disease-representative cohorts that are free of confounding factors.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it