Dietary Patterns in Runners with Gastrointestinal Disorders
Bibliographic record
Abstract
Individuals with inflammatory bowel disease (IBD), irritable bowel syndrome (IBS) and reflux frequently experience gastrointestinal symptoms (GIS), potentially enhanced by high-intensity running. Food avoidances, food choices, and GIS in runners with IBS/IBD (n = 53) and reflux (n = 37) were evaluated using a reliability and validity tested questionnaire. Comparisons to a control group of runners (n = 375) were made using a Fisher’s Exact test. Runners with IBS/IBD experienced the greatest amount of exercise-induced GIS followed by those with reflux. Commonly reported GIS were stomach pain/cramps (77%; 53%), bloating (52%; 50%), intestinal pain/cramps (58%; 33%), and diarrhea (58%; 39%) in IBS/IBD and reflux groups respectively. In the pre-race meal, those with IBS/IBD frequently avoided milk products (53%), legumes (37%), and meat (31%); whereas, runners with reflux avoided milk (38%), meat (36%), and high-fibre foods (33%). When considering food choices pre-race, runners with IBS/IBD chose grains containing gluten (40%), high fermentable oligo-, di-, mono-saccharides and polyols (FODMAP) fruits (38%), and water (38%). Runners with reflux chose water (51%), grains containing gluten (37%), and eggs (31%). In conclusion, while many runners with IBS/IBD and reflux are avoiding trigger foods in their pre-race meals, they are also consuming potentially aggravating foods, suggesting nutrition advice may be warranted.
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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 | 0.001 |
| 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) | 0.001 | 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".