P856 Associations of antibiotics, hormonal therapies, oral contraceptives, and long-term NSAIDs with Inflammatory Bowel Disease: results from the Prospective Urban Rural Epidemiology (PURE) study
Bibliographic record
Abstract
Abstract Background Several medications have been considered to contribute to the aetiology of inflammatory bowel disease (IBD). This study assessed the association between medication use and risk of developing IBD using the Prospective Urban Rural Epidemiology (PURE) cohort. Methods This was a prospective cohort study of 133,137 individuals between the ages of 20-80 from 24 countries. Country-specific validated questionnaires documented baseline and follow-up medication use. Participants were followed prospectively at least every 3 years. The main outcome was development of IBD, including Crohn’s disease (CD) and ulcerative colitis (UC). Short-term (baseline but not follow-up use) and long-term use (baseline and subsequent follow-up use) was evaluated. Results are presented as adjusted odds ratios (aOR) with 95% confidence intervals (CI). Results During the median follow-up of 11.0 years [interquartile range (IQR) 9.2-12.2], we recorded 571 incident cases of IBD (143 CD and 428 UC). Higher risk of incident IBD was associated with baseline antibiotic use [aOR: 2.81 (95% CI: 1.67-4.73), p=0.0001] and hormonal medication use [aOR: 4.43 (95% CI: 1.78-11.01), p=0.001]. Among females, previous or current oral contraceptive use was also associated with IBD development [aOR: 2.17 (95% CI: 1.70-2.77), p=5.02E-10]. NSAID users were also observed to have increased risk of IBD [aOR: 1.80 (95% CI: 1.23-2.64), p=0.002], which was driven by long-term users [aOR: 5.58 (95% CI: 2.26-13.80), p<0.001]. All significant results were consistent in direction for CD and UC with low heterogeneity. Conclusion Antibiotics, hormonal medications, oral contraceptives, and long-term NSAID use were associated with increased odds of incident IBD after adjustment for covariates.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".