A Meta-Analysis of Randomized Controlled Trials of Aspirin in Primary Prevention of Cardiovascular Disease.
Bibliographic record
Abstract
Abstract Abstract 174 Primary prevention with aspirin reduces the risk of non-fatal cardiovascular events but has not been demonstrated to reduce mortality. We performed an updated meta-analysis of randomised controlled trials of aspirin in primary prevention to obtain best estimates of the benefits and harm of aspirin compared with no aspirin with a focus on mortality. Eligible articles were identified by computerized search of MEDLINE, EMBASE, Cochrane library and CINAHL databases, review of bibliographies of relevant publications and a related article search using PubMed. The outcomes of interest included all cause mortality, cardiovascular mortality, the composite of myocardial infarction, stroke or death, and bleeding. 2 reviewers independently extracted study information and data. Data were pooled from individual trials using the DerSimonian-Laird random-effects model and results are presented as relative risk (RR) and 95% confidence intervals (CI). 8 studies comprising a total of 96,726 subjects were included. Aspirin reduced all-cause mortality (RR 0.94; 95%CI 0.88–1.00), the composite of myocardial infarction, stroke or cardiovascular death (RR 0.87; 95%CI 0.82–0.93), and myocardial infarction (RR 0.8; 95%CI 0.66–0.98) but did not significantly reduce cardiovascular mortality (RR 0.94; 95%CI 0.82–1.08) or stroke (RR 0.93; 95%CI 0.81–1.07). Aspirin increased the risk of major bleeding (RR; 1.69 95%CI 1.38–2.08), gastrointestinal bleeding (RR 1.38; 95%CI 1.16–1.65) and hemorrhagic stroke (RR 1.36; 95%CI 1.01–1.84). There was no interaction between subjects with or without diabetes for the outcomes of all cause mortality, cardiovascular mortality, the composite of myocardial infarction, stroke or death. Aspirin therapy in subjects with no prior history of cardiovascular disease reduces the risk of cardiovascular events, myocardial infarction and overall mortality. These benefits are achieved at the expense of increased bleeding. Disclosures: No relevant conflicts of interest to declare.
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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 | 0.045 | 0.086 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.028 | 0.057 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".