Influenza vaccines may protect against cardiovascular diseases: The evidence is mounting and should be known by the Canadian public health community
Bibliographic record
Abstract
Evidence on the protective effect of influenza vaccines to prevent cardiovascular disease (CVD) is mounting. We identified 28 systematic reviews/meta-analyses on the effect of influenza vaccines on CVD using different research questions, data sources, selection criteria and outcomes. Most results leaned towards a protective effect. Results of recently published experimental and observational studies not included in these reviews were going in the same direction. The evidence is very robust for cardiovascular deaths and nonfatal myocardial infarction in high-risk individuals, but lower for heart failure, arrhythmia, and stroke and also for all outcomes in low-risk adults. There is also limited evidence for pneumococcal polysaccharide vaccines and evidence has to be collected from ongoing trials on respiratory syncytial virus vaccines. Up to now, this effect has not been considered in economic evaluations of influenza vaccines and its inclusion may change CVD results markedly. This effect is not mentioned in the Canadian Immunization Guide and not known by a majority of vaccinators. The objective of this short commentary is to alert the Canadian public health community and to provide information that could be used at the field level to promote the usefulness of influenza vaccines.
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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 | 0.015 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".