What Are Adults With Inflammatory Bowel Disease (IBD) Eating? A Closer Look at the Dietary Habits of a Population‐Based Canadian IBD Cohort
Bibliographic record
Abstract
BACKGROUND: A comprehensive study of what individuals with inflammatory bowel disease (IBD) are eating that encompasses food avoidance, dietary sugar consumption, and a comparison with the non-IBD Canadian population has not been documented. The aim was to analyze these interrelated dietary components. METHODS: Food avoidance and sugar intake data were collected from 319 patients with IBD enrolled in the University of Manitoba IBD Cohort Study. Diets of those with IBD (n = 256) were compared with a matched, non-IBD Canadian cohort using the nutrition questions obtained from the Canadian Health Measures Survey (CHMS). RESULTS: Food avoidance among IBD is prevalent for alcohol, popcorn, legumes, nuts, seeds, deep-fried food, and processed deli meat, with a higher prevalence among those with active IBD. Patients with active IBD also consumed significantly more portions of sports drinks and sweetened beverages compared with those with inactive disease. Compared with the non-IBD Canadian population, patients with IBD consume significantly less iron-rich food but more milk. CONCLUSIONS: Food avoidance is common among those with IBD but may be due more to personal preferences, while sugar-laden beverages may be displacing other foods higher in nutrients. The overall diet of patients with IBD differed from that of the non-IBD Canadian population, but deficiencies were observed in both groups. Considering malnutrition among persons living with IBD, nutrition education by trained dietitians as part of the IBD team is imperative to address food avoidance and overall balance nutrition as part of treating and preventing nutrition deficiencies.
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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 | 0.003 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".