Influenza Vaccination: Accelerating the Process for New Vaccine Development in Older Adults
Bibliographic record
Abstract
Increased susceptibility to the serious complications of influenza is common in older adults. It is often ascribed to weakening of the immune system with age, and 90% of influenza-related deaths occur in older adults despite widespread vaccination programs. Common chronic conditions not only contribute to the loss of immune protection after vaccination and increase the risk for serious outcomes of influenza, but also increase the long-term consequences following hospitalization. Interactions of T and B cell ageing, chronic elevation of inflammatory cytokines (sometimes dubbed "inflammaging"), and dysregulated acute cytokine production pose major challenges to the development of new and more effective vaccines. However, these age-related problems are modifiable, as we have shown, and provide a clear margin for improvement. This chapter describes how an exclusive focus on developing influenza vaccines to stimulate strain-specific antibody responses against the hemagglutinin surface glycoprotein of the influenza virus, to the exclusion of other potentially important mechanisms, is missing the mark in terms of preventing the serious complications of influenza in older adults. Novel approaches are needed to enhance antibody-mediated protection against infection and stimulate cell-mediated immune responses to clear influenza virus from the lungs. These strategies for improving vaccine effectiveness will address the public health need for "vaccine prevention of disability" to mitigate the global pressures of aging populations on health and social care systems.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".