Micronutrients are associated with endoscopic postoperative recurrence in Crohn’s disease: a multicenter prospective cohort study in North america
Bibliographic record
Abstract
BACKGROUND AND AIMS: Diet may influence the disease course in inflammatory bowel disease, but its role in postoperative outcomes for Crohn's disease (CD) remains unclear. This study aimed to assess the association of macro- and micronutrient intake with endoscopic postoperative recurrence (ePOR) in a prospective multicenter cohort. METHODS: Patients with CD following ileocolic resection were prospectively recruited from six North American centers. Primary study outcome was ePOR (modified Rutgeerts' score ≥i2a) during follow-up. Nutritional intake was assessed through 2-day food diaries verified by dietitian-delivered interview. Associations between nutrient intake and ePOR were evaluated. Random forest models with 10-fold cross-validation assessed the predictive value of nutrients and clinical factors for ePOR. RESULTS: A total of 520 food diaries from 103 patients were analyzed; 37 patients (36%) experienced ePOR. Univariate analysis identified 8 nutrients associated with ePOR including lower intake of isoflavones (genistein, daidzein, glycitein; P < .01), inositol (P < .01), pinitol (P = .02), provitamin-A carotenoid (P < .01), xylitol (P = .03) and parinaric Acid (P = .03). Sensitivity analysis confirmed the association of lower intake of all isoflavones (P < .01) and pinitol (P = .04) with ePOR at first postoperative colonoscopy. Random forest models showed poor discrimination for clinical factors alone (area under the curve [AUC] 0.59) but an acceptable discrimination for nutrients alone (AUC 0.67), which improved when combining nutrients and clinical factors (AUC 0.71). CONCLUSION: Lower intake of specific micronutrients is associated with ePOR in CD patients. A machine-learning model combining nutrient intake and clinical factors enhanced the prediction of ePOR. These findings highlight the importance of postoperative nutritional assessment and suggest dietary interventions may help prevent postoperative recurrence in CD.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".