Infectious diseases, cardio-cerebrovascular health and vaccines: pathways to prevention
Bibliographic record
Abstract
Cardiovascular and infectious diseases both feature among the leading causes of death among men and women in the world. The pathophysiological pathways of infection and cardiovascular disease intersect, and there is a bidirectional relationship between the two. Vaccines are available for the most common infectious diseases affecting older adults, such as influenza, pertussis, pneumococcal disease, herpes zoster, COVID and respiratory syncytial virus (RSV). In many countries, these vaccines are recommended systematically for older adults and any adults with comorbidities, who are also those most likely to suffer from cardiovascular disease. There is a large body of evidence attesting to the benefits of vaccination on cardio- and cerebrovascular health. The European Interdisciplinary Council for Aging (EICA) and the Italian Society for Cardiovascular Prevention (Società Italiana per la Prevenzione Cardiovascolare, SIPREC) convened a 2-day meeting in June 2024 to review the state of the evidence on the relationship between cardio- and cerebrovascular health and the most common infectious diseases, and the role of vaccines in preventing both infection and its adverse consequences in terms of cardiovascular and cerebrovascular outcomes. We present here the Executive Summary of the proceedings of this meeting.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".