Effect of low‐dose aspirin on health outcomes: An umbrella review of systematic reviews and meta‐analyses
Bibliographic record
Abstract
AIMS: This study aimed to use an umbrella review methodology to capture the range of outcomes that were associated with low-dose aspirin and to systematically assess the credibility of this evidence. METHODS: Aspirin is associated with several health outcomes, but the overall benefit/risk balance related to aspirin use is unclear. We searched three major databases up to 15 August 2019 for meta-analyses of observational studies and randomized controlled trials (RCTs) including low-dose aspirin compared to placebo or other treatments. Based on random-effects summary effect sizes, 95% prediction intervals, heterogeneity, small-study effects and excess significance, significant meta-analyses of observational studies were classified from convincing (class I) to weak (class IV). For meta-analyses of RCTs, outcomes with random effects P-value < .005 and a moderate/high GRADE assessment, were classified as strong evidence. From 6802 hits, 67 meta-analyses (156 outcomes) were eligible. RESULTS: Observational data showed highly suggestive evidence for aspirin use and increased risk of upper gastrointestinal bleeding (RR = 2.28, 95% CI: 1.97-2.64). In RCTs of low-dose aspirin, we observed strong evidence for lower risk of CVD in people without CVD (RR = 0.83; 95% CI: 0.79-0.87) and in general population (RR = 0.83; 95% CI: 0.79-0.89), higher risk of major gastrointestinal (RR = 1.47; 95% CI: 1.26-1.72) and intracranial bleeding (RR = 1.34; 95% CI: 1.18-1.53), and of major bleedings in people without CVD (RR = 1.62; 95% CI: 1.26-2.08). CONCLUSION: Compared to other active medications, low-dose aspirin had strong evidence for lower risk of bleeding, but also lower comparative efficacy. Low-dose aspirin significantly lowers CVD risk and increases risk of bleeding. Evidence for multiple other health outcomes is limited.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.032 | 0.011 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".