Bile acid alterations associated with indolent course of inflammatory bowel disease
Bibliographic record
Abstract
The indolent course of treatment-naive patients with inflammatory bowel disease (IBD) is confirmed predictable based on clinical characteristics. Current evidences supported that bile acids (BAs) alteration might be promising biomarkers in the field of IBD. We aimed to analyze the alterations of BAs as the disease progresses and explore their predictive value for indolent course of IBD. The indolent course of IBD was defined as a disease course without need for strict interventions throughout the entire follow-up. A targeted metabolomics method was used to detect the concentration of 27 BAs from serum sample in treatment-naive patients with IBD (Crohn's disease [CD], n = 27; ulcerative colitis [UC], n = 50). Patients with CD and UC were individually divided into two groups for further study according to the median time of indolent course. The overall BAs profile and the clinical value of BAs in predicting indolent course of IBD were identified between different groups. For CD, the levels of deoxycholic acid, glycodeoxycholic acid, taurodeoxycholic acid, glycolithocholic acid-3-sulfate disodium salt and iso-lithocholic acid were significantly increased in patients with indolent course > 18 M (p < 0.05). These five BAs owned 83.5% accuracy for predicting indolent course over 18 months in CD. For UC, the concentration of deoxycholic acid and glycodeoxycholic acid were significantly higher, while dehydrocholic acid were lower in patients with indolent course > 48 M (p < 0.05). These three BAs predicted indolent course over 48 months of 69.8% accuracy in UC. The specific BAs alterations might be potential biomarkers in predicting disease course of IBD patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".