Does influenza vaccination contribute to the prevention of cardiovascular events? An umbrella review
Bibliographic record
Abstract
Background: There is a growing body of evidence on the potential benefit of influenza vaccination against the occurrence of cardiovascular (CV) events. Objective: This umbrella review of systematic reviews and meta-analyses (SRMAs) aims to summarize the available evidence on the risk of CV events in adults after receipt of influenza vaccine. Methods: Four electronic databases were searched (CINAHL, PubMed, SYSVAC and Cochrane Library) for SRMAs published in English or French, between January 1, 2000, and January 14, 2025. Eligible SRMAs included those with a quantitative synthesis of data examining the association between influenza vaccination and the risk of CV events in adults. Data from the included SRMAs were extracted using predefined variables. The quality of each SRMA was assessed by two independent reviewers using the AMSTAR 2 tool. Results: The review included 25 SRMAs published between 2012 and 2024. Overall, 15 SRMAs were deemed to be of moderate or high quality and were further considered in the evidence synthesis. The most frequently evaluated clinical outcomes were myocardial infarction (MI), all-cause and CV mortality, and major adverse cardiovascular events (MACE). In vaccinated individuals at high-risk for CV events, the risk of CV death was significantly reduced by 23% to 47%, MACE by 26% to 37%, MI by 29% to 34%, and stroke by 13% to 19% compared to unvaccinated individuals. Conclusion: High-quality evidence from the existing literature supports influenza vaccination as an effective preventive measure for reducing CV disease burden. Highlighting this benefit to patients could increase vaccine uptake and improve both influenza and CV outcomes, especially where coverage remains suboptimal.
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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 | 0.021 | 0.080 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.017 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".