Aspirin Use as a Risk Factor for Marginal Ulceration in Roux-en-Y Gastric Bypass Patients: A Meta-Analysis of 24,770 Patients
Bibliographic record
Abstract
Background Roux-en-Y gastric bypass (RYGB) is a recognized, safe bariatric procedure with minimal complications. Marginal ulceration, however, remains a challenging problem with an incidence of 8-12%. While chronic NSAID use is an established risk factor for ulcer formation, aspirin use itself as a cause for marginal ulceration is still unclear. We aim to compare the rates of marginal ulceration in RYGB with and without aspirin use. Methods PubMed, ScienceDirect, Cochrane, Web of Science, and Google Scholar were searched for articles between 2008 and 2021 by two independent reviewers using the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA). The risk of bias was assessed using Newcastle-Ottawa Scale. Meta-analysis was conducted using a fixed-effect model. Results From 5324 studies screened, we included 3 studies. Two studies had a low risk of bias, and the other one presented a high risk of bias on the Newcastle-Ottawa Scale. We included 24,770 patients, 1911 with aspirin use and 22,859 without aspirin use. After the meta-analysis, patients who used aspirin had a significantly higher marginal ulceration rate than those who did not (OR = 1.33 [95% CI 1.08 to 1.63], P < .002; I 2 = 39%). Conclusions Aspirin use is associated with increased rates of marginal ulceration after RYGB.
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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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.051 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".