Bibliographic record
Abstract
PURPOSE OF REVIEW: Apparently conflicting meta-analysis results have led to renewed debate about the role of aspirin for the primary prevention of cardiovascular disease. We review the results of meta-analyses comparing aspirin with placebo or no aspirin for the primary prevention of cardiovascular disease and critically evaluate whether aspirin provides a net benefit. RECENT FINDINGS: The results of four independently conducted meta-analyses between 2009 and 2012 involving between 95 000 and 102 621 individuals at low risk of cardiovascular disease are consistent with the results of the 2002 Antithrombotic Trialists' Collaboration meta-analysis, which found that aspirin reduces cardiovascular events primarily by reducing nonfatal myocardial infarction (MI). There is no convincing evidence that aspirin reduces cardiovascular mortality, but estimates from all of the meta-analyses suggest a modest reduction in all-cause mortality. Aspirin reduces ischaemic stroke but increases haemorrhagic stroke and major bleeding. SUMMARY: The meta-analysis results consistently indicate that, in individuals at low risk for cardiovascular disease, aspirin reduces the risk of MI at the cost of an increase in major bleeding and produces a modest nominally significant reduction in total mortality. These results suggest that recommendations for primary prevention with aspirin should be individualized, taking into account the balance between benefits and risks and individual values and preferences.
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 | 0.016 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.004 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".