Roadmap for Sex-Responsive Influenza and COVID-19 Vaccine Research in Older Adults
Bibliographic record
Abstract
Sex differences in the immune system are dynamic throughout the lifespan and contribute to heterogeneity in the risk of infectious diseases and the response to vaccination in older adults. The importance of the intersection between sex and age in immunity to viral respiratory diseases is clearly demonstrated by the increased prevalence and severity of influenza and COVID-19 in older males compared to older females. Despite sex and age biases in the epidemiology and clinical manifestations of disease, these host factors are often ignored in vaccine research. Here, we review sex differences in the immunogenicity, effectiveness, and safety of the influenza and COVID-19 vaccines in older adults and the impact of sex-specific effects of age-related factors, including chronological age, frailty, and the presence of comorbidities. While a female bias in immunity to influenza vaccines has been consistently reported, understanding of sex differences in the response to COVID-19 vaccines in older adults is incomplete due to small sample sizes and failure to disaggregate clinical trial data by both sex and age. For both vaccines, a major gap in the literature is apparent, whereby very few studies investigate sex-specific effects of aging, frailty, or multimorbidity. By providing a roadmap for sex-responsive vaccine research, beyond influenza and COVID-19, we can leverage the heterogeneity in immunity among older adults to provide better protection against vaccine-preventable diseases.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".