Immunosenescence: implications for vaccination programs in the elderly
Bibliographic record
Abstract
Dessi Loukov,1,2,* Avee Naidoo,1,2,* Dawn ME Bowdish1,21Department of Pathology and Molecular Medicine, McMaster Immunology Research Centre, McMaster University, Hamilton, ON, Canada; 2Department of Pathology and Molecular Medicine, Institute for Infectious Diseases Research, McMaster University, Hamilton, ON, Canada *These authors contributed equally to this work Abstract: Worldwide, infectious disease is responsible for much of the morbidity and mortality in the elderly. As the number of individuals over the age of 65 increases, the economic and social costs of treating these infections will become a major challenge. Vaccination is the most effective and least costly preventative measure in our arsenal; however, vaccines that are effective in children and young adults are often ineffective in older adults. This is a result of the deterioration in immune function that occurs with age, referred to as immunosenescence. Age-associated changes in leukocyte phenotype and function impair primary vaccine responses and weaken long-lasting memory responses. In this review, we discuss current vaccination approaches in the elderly and strategies to improve responsiveness in older adults, which include increasing vaccine immunogenicity and overcoming the fundamental immune defects that prevent optimal immune responses. Keywords: immunosenescence, vaccination, elderly, influenza, pneumonia, zoster
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".