The complex relationship between diet, symptoms, and intestinal inflammation in persons with inflammatory bowel disease: The Manitoba Living With IBD Study
Bibliographic record
Abstract
BACKGROUND: We aimed to examine whether an association exists between diet quality, based on the Prospective Urban Rural Epidemiology (PURE) Healthy Diet Score (HDS), and active inflammatory bowel disease (IBD). METHODS: Participants were drawn from the Manitoba Living With IBD Study cohort. The Harvard Food Frequency Questionnaire (FFQ) was used to calculate the HDS at two time points: baseline and 1-year follow-up. Using generalized estimating equations (GEE) logistic regression, we assessed the association between the HDS and (1) the IBD Symptom Inventory (IBDSI); (2) intestinal inflammation, measured by fecal calprotectin (FCAL); and (3) self-reported IBD flares. RESULTS: There were 294 completed FFQs among 153 people. Of these, 100% had completed data about an IBD flare, 98% had FCAL measurements, and 96% had completed IBDSI scores. On a HDS scoring method of 0-8, the odds of FCAL >250 mcg/g were lower for participants with a HDS of 4 vs 0-3 (adjusted odds ratio [OR], 0.38; 95% CI, 0.19-0.77). When applying a second HDS scoring method (8-40), the odds of having an IBD flare were 3.6 times greater with a HDS between 21 and 24 compared with an HDS ≤20 (adjusted OR, 3.63; 95% CI, 1.03-12.78). CONCLUSIONS: We found that active inflammation was less likely among those with a moderate HDS , whereas symptomatic IBD flares were more likely. People may choose to consume a moderate amount of healthy foods such as fruits and vegetables, even knowing that those foods may cause a symptomatic flare.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".