Influenza vaccination: a ‘shot’ at INVESTing in cardiovascular health
Bibliographic record
Abstract
The link between viral respiratory infection and non-pulmonary organ-specific injury, including cardiac injury, has become increasingly appreciated during the current coronavirus disease 2019 (COVID-19) pandemic. Even prior to the pandemic, however, the association between acute infection with influenza and elevated cardiovascular risk was evident. The recently published results of the NHLBI-funded INfluenza Vaccine to Effectively Stop CardioThoracic Events and Decompensated (INVESTED) trial, a 5200 patient comparative effectiveness study of high-dose vs. standard-dose influenza vaccine to reduce cardiopulmonary events and mortality in a high-risk cardiovascular population, found no difference between strategies. However, the broader implications of influenza vaccine as a strategy to reduce morbidity in high-risk patients remain extremely important, with randomized controlled trial and observational data supporting vaccination in high-risk patients with cardiovascular disease. Given a favourable risk-benefit profile and widespread availability at generally low cost, we contend that influenza vaccination should remain a centrepiece of cardiovascular risk mitigation and describe the broader context of underutilization of this strategy. Few therapeutics in medicine offer seasonal efficacy from a single administration with generally mild, transient side effects, and exceedingly low rates of serious adverse effects. Infection control measures such as physical distancing, hand washing, and the use of masks during the COVID-19 pandemic have already been associated with substantially curtailed incidence of influenza outbreaks across the globe. Appending annual influenza vaccination to these measures represents an important public health and moral imperative.
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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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.012 | 0.018 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".