Strongly increased risk of gastric and duodenal ulcers among new users of low‐dose aspirin: results from two large cohorts with new‐user design
Bibliographic record
Abstract
BACKGROUND: Low-dose aspirin is a risk factor for peptic ulcer disease but previous, population-based cohort studies may have underestimated the low-dose aspirin risk because they did not use a new-user design. Gastrointestinal bleeding occurs more frequently early after initiation of low-dose aspirin therapy than in later years. AIM: To assess the associations of low-dose aspirin with gastric and duodenal ulcer incidence in prevalent- and new-user design. METHODS: Multivariate Cox regression models in the German ESTHER study (N = 7737) and the UK Biobank (N = 213,598) with more than 10 years of follow-up. RESULTS: In the prevalent-user design, there was no significant association between low-dose aspirin and gastric ulcer observed in both cohorts. Furthermore, low-dose aspirin was weakly, statistically significantly associated with prevalent duodenal ulcer in the UK Biobank (hazard ratio [95% confidence interval]: 1.27 [1.07-1.51]) but not in the ESTHER study (1.33 [0.54-3.29]). When restricting the exposure to only new users, the hazard ratios for incident gastric and duodenal ulcer disease were 1.82 [1.58-2.11] and 1.66 [1.36-2.04] in the UK Biobank, respectively, and 2.83 [1.40-5.71] and 3.89 [1.46-10.42] in the ESTHER study, respectively. CONCLUSIONS: This study shows that low-dose aspirin is an independent risk factor for both gastric and duodenal ulcers. The associations were not significant or weak in the prevalent-user design and strong and statistically significant in the new-user design in both cohorts. Thus, it is important to weigh risks against benefits when low-dose aspirin treatment shall be initiated and to monitor adverse gastrointestinal symptoms after the start of low-dose aspirin therapy.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".