Aspirin for the Primary Prevention of Vascular Ischemic Events: An Updated Systematic Review and Meta-analysis to Support Shared Decision-Making
Bibliographic record
Abstract
Background: Since the publication of the 2010 Canadian antiplatelet guidelines, several large randomized controlled trials (RCTs) have evaluated the role of aspirin (ASA) use in primary prevention. We evaluated the effect of ASA use, compared with no ASA, on ischemic and bleeding events in patients without known atherosclerotic cardiovascular diseases. Methods: We updated a published systematic review and meta-analysis by searching MEDLINE, Embase, and CENTRAL for the period up to March 2023. We included RCTs that enrolled patients for primary prevention of atherosclerotic cardiovascular diseases, and compared use of ASA to no ASA. We assessed risk of bias (RoB) using the Cochrane RoB tool, and certainty of evidence using the grading recommendations, assessment, development, and evaluation (GRADE) criteria. The primary efficacy outcome was major adverse cardiovascular events (MACE) (death, myocardial infarction, or stroke). The primary safety outcomes were intracranial hemorrhage and extracranial major bleeding events. We used a random-effects model to generate pooled risk ratios (RRs) and 95% confidence intervals (CIs). Results: We included 14 RCTs (n = 167,587) at overall low RoB, with a median follow-up of 5 years. Compared to no ASA, ASA use reduced the incidence of MACE (RR 0.90, 95% CI 0.86-0.94), with a higher risk of intracranial hemorrhage (RR 1.33, 95% CI 1.13-1.56) and extracranial major bleeding (RR 1.67, 95% CI 1.36-2.06). In prespecified subgroups of age, sex, and diabetes, effect estimates were consistent. Conclusions: ASA use in primary prevention is associated with a consistent reduction in MACE, but at the expense of major bleeding events. Patient values and preferences should be taken into account when considering ASA use for primary prevention.
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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 | 0.034 | 0.100 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.033 |
| Bibliometrics | 0.015 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".