Vaccination in Patients with Cardiovascular Disease: A Case-Based Approach and Contemporary Review
Bibliographic record
Abstract
Vaccination is a crucial preventative strategy, particularly in individuals with cardiovascular (CV) disease (CVD). People living with CVD are at increased risk of morbidity and mortality from vaccine-preventable infections such as influenza, severe acute respiratory syndrome-corona virus 2 (SARS-CoV-2), respiratory syncytial virus (RSV), varicella zoster virus (VZV), and pneumococcal disease. These infections also have been associated with downstream CV complications, including ischemic events and myocarditis. Randomized controlled trials have demonstrated that influenza vaccination reduces major adverse CV events and all-cause mortality, especially in people with CVD. The same has been observed in registry analyses during the SARS-CoV-2 pandemic. Pooling of data from observational and cohort studies also has shown significant benefit of vaccination against RSV, VZV, and pneumococcal disease in older populations and those with CV comorbidities. Despite recommendations from national public health guidelines and immunization programs, vaccination uptake in patients with CVD remains suboptimal. This low uptake is influenced by lack of vaccine information, access issues, and mistrust in the healthcare system, all summarized in the term "vaccine hesitancy." Vaccination promotion should focus on addressing these gaps in communication and access barriers at the provider, community, and public health levels. Healthcare providers including cardiologists are reminded, through this review, of the importance of emphasizing vaccination recommendations during clinical encounters. Addressing patient misconceptions and providing patient decision aids strongly improves acceptance rates. Continued efforts at the community and public health levels should address barriers to access and advance surveillance methods to target improved clinical outcomes for groups at risk.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".