The epidemiologic and biologic basis for classifying older age as a high-risk, immunocompromising condition for pneumococcal vaccine policy
Bibliographic record
Abstract
INTRODUCTION: . Pneumococcal vaccine recommendations are often limited to adults with specific medical conditions despite similar disease risks among older adults due to immunosenescence. AREAS COVERED: This article reviews epidemiologic, biologic, and clinical evidence supporting the consideration of older age due to immunosenescence as an immunocompromising condition for the purpose of pneumococcal vaccine policy and the role vaccination can play in healthy aging. EXPERT OPINION: Epidemiologic and biologic evidence suggest that pneumococcal disease risk increases with age and is comparable for healthy older adults and younger adults with immunocompromising conditions. Because immunocompromising conditions are already indicated for pneumococcal conjugate vaccines (PCVs), a comprehensive public health strategy would also recognize immunosenescence. Moreover, older persons should be vaccinated before reaching the highest risk ages, consistent with the approach for other immunocompromising conditions. To facilitate PCV use among older adults, vaccine technical committees (VTCs) could classify older age as an immunocompromising condition based on the process of immunosenescence. With global aging, VTCs will need to consider immunosenescence and vaccine use during healthy aging.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| 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".