A143 FOOD AVOIDANCE IN PATIENTS WITH INFLAMMATORY BOWEL DISEASE: WHAT, WHEN AND WHO?
Bibliographic record
Abstract
Patients with inflammatory bowel diseases are known to avoid a variety of foods. However, it remains unclear how this behavior varies across patients. This cross-sectional study aimed to describe food avoidance in these patients and investigate whether it varied according to disease’s activity, disease’s subtype, Crohn’s location, and prior history of bowel resection, strictures, and fistulae. Outpatients with Crohn’s disease (n=173) and ulcerative colitis (n=72) reported which food they avoid during remission and active disease using a list of 82 food items classified in 10 food categories. Medical charts were reviewed for patients’ characteristics. Linear regression analyses were used to compare food exclusion rates between inflammatory bowel disease subgroups and food categories. In total, 75% of patients reported food avoidance behavior during remission. Food exclusion rates varied from 1 to 39%. Most avoided foods were those with capsaicin, meat alternatives, and raw vegetables. Overall, food exclusion rates were 69% higher in active disease than in remission (P<0.001), 38% higher in Crohn’s disease than ulcerative colitis (P<0.001), and 50% higher in stricturing than non-stricturing Crohn’s disease (P<0.001). No association was found with other disease characteristics. The avoided foods were very similar across patients except for alcoholic beverages and foods rich in dietary fibers/residue, which were avoided more specifically in active disease and Crohn’s disease, respectively. Food avoidance is common in inflammatory bowel disease but varies according to disease characteristics. Future nutrition research should consider that inflammatory bowel disease patients may respond differently to diet modification. None
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".