A Prospective Population-Based Study of Triggers of Symptomatic Flares in IBD
Bibliographic record
Abstract
OBJECTIVES: We aimed to determine whether any of the nonsteroidal anti-inflammatory drugs (NSAIDs), antibiotics, infections, and stress trigger symptomatic flares of inflammatory bowel diseases (IBDs). METHODS: Participants drawn from a population-based IBD research registry were surveyed every 3 months for 1 year. They simultaneously tracked the use of NSAIDs, antibiotics, infections, major life events, mood, and perceived stress. Social networks, childhood socioeconomic status, and smoking were assessed at baseline. Disease flare was identified using the Manitoba Inflammatory Bowel Disease Index, a validated disease activity index. Across any two consecutive survey periods, participants were categorized as having a flare (inactive/active), having no flare (inactive/inactive), or remaining active (active/active). Potential triggers were evaluated for the first 3-month period to determine predictive rather than concurrent relationships. Data from only one pair of 3-month periods from an individual were analyzed. RESULTS: A total of 704 participants completed the baseline survey; 552 (78.3%) returned all 5 surveys. In all, 174 participants who had a flare were compared with 209 who had no flare. Perceived stress, negative affect (mood), and major life events were the only trigger variables significantly associated with flares. There were no differences between those who flared and those who did not, in the use of NSAIDs, antibiotics, or in the presence of infections. Multivariate logistic regression analyses indicated that only high-perceived stress (adjusted odds ratio=2.40 (1.35, 4.26)) was associated with an increased risk of flare. CONCLUSIONS: This study adds to the growing evidence that psychological factors contribute to IBD symptom flares. There was no support for differential rates of use of NSAIDS, antibiotics, or for the occurrence of (non-enteric) infections related to IBD flares.
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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 | 0.002 |
| 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".