Low-Dose Acetylsalicylic Acid Use and the Risk of Upper Gastrointestinal Bleeding: A Meta-Analysis of Randomized Clinical Trials and Observational Studies
Bibliographic record
Abstract
BACKGROUND: Low-dose acetylsalicylic acid (LDA, 75 mg/day to 325 mg/day) is recommended for primary and secondary prevention of cardiovascular events, but has been linked to an increased risk of upper gastrointestinal bleeding (UGIB). OBJECTIVE: To analyze the magnitude of effect of LDA use on UGIB risk. METHODS: The PubMed and Embase databases were searched for randomized controlled trials (RCTs) reporting UGIB rates in individuals receiving LDA, and observational studies of LDA use in patients with UGIB. Studies were pooled for analysis of UGIB rates. RESULTS: Eighteen studies were included. Seven RCTs reported UGIB rates in individuals randomly assigned to receive LDA (n=22,901) or placebo (n=22,923). Ten case-control studies analyzed LDA use in patients with UGIB (n=10,816) and controls without UGIB (n=30,519); one cohort study reported 207 UGIB cases treated with LDA only. All studies found LDA use to be associated with an increased risk of UGIB. The mean number of extra UGIB cases associated with LDA use in the RCTs was 1.2 per 1000 patients per year (95% CI 0.7 to 1.8). The number needed to harm was 816 (95% CI 560 to 1500) for RCTs and 819 (95% CI 617 to 1119) for observational studies. Meta-analysis of RCT data showed that LDA use was associated with a 50% increase in UGIB risk (OR 1.5 [95% CI 1.2 to 1.8]). UGIB risk was most pronounced in observational studies (OR 3.1 [95% CI 2.5 to 3.7]). CONCLUSIONS: LDA use was associated with an increased risk of UGIB.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.030 | 0.059 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.045 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".