Influenza Vaccination for the Prevention of Cardiovascular Disease in the Americas: Consensus document of the Inter-American Society of Cardiology and the Word Heart Federation
Bibliographic record
Abstract
Background: Cardiovascular mortality is decreasing but remains the leading cause of death world-wide. Respiratory infections such as influenza significantly contribute to morbidity and mortality in patients with cardiovascular disease. Despite of proven benefits, influenza vaccination is not fully implemented, especially in Latin America. Objective: The aim was to develop a regional consensus with recommendations regarding influenza vaccination and cardiovascular disease. Methods: A multidisciplinary team composed by experts in the management and prevention of cardiovascular disease from the Americas, convened by the Inter-American Society of Cardiology (IASC) and the World Heart Federation (WHF), participated in the process and the formulation of statements. The modified RAND/UCLA methodology was used. This document was supported by a grant from the WHF. Results: An extensive literature search was divided into seven questions, and a total of 23 conclusions and 29 recommendations were achieved. There was no disagreement among experts in the conclusions or recommendations. Conclusions: There is a strong correlation between influenza and cardiovascular events. Influenza vaccination is not only safe and a proven strategy to reduce cardiovascular events, but it is also cost saving. We found several barriers for its global implementation and potential strategies to overcome them.
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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 | 0.025 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".