Immune responses of COVID-19 vaccines in older adults: a systematic review and meta-analysis
Bibliographic record
Abstract
Introduction In the wake of the unprecedented global COVID-19 pandemic, the development of effective vaccines is crucial. While multiple vaccines have been developed, evidence suggests that immune response may vary especially in older adults. Evaluation of COVID-19 immunogenicity in this vulnerable population needs to be explored. Methods Literature search was performed in databases including PubMed, ScienceDirect, and Scopus. The quality of studies was evaluated using the Cochrane Risk of Bias 2.0 and Newcastle Ottawa Scale for Cohort Studies. Results There are 10 studies included in this review. Two doses of the COVID-19 vaccine showed a significant increase in antibody titers (MD 221.48 AU/mL; 95% CI 109.16-333.80; p=0.0001). Subgroup analysis showed a dose-dependent relationship, with the highest amount of increase in MVC-COV19 at 25 mcg. The increase in anti-RBD IgG was also significant (MD 3.355 log 10 AU/mL; 95% CI 3.03-4.08; p=0.00001). S1 reactive T cells and anti-S1 IgG was not significantly increased after 2 doses of the vaccine. Conclusion Our study found that in general, the COVID-19 vaccine is effective in increasing several immune parameters. Measures to increase efficacy in this population such as a third dose or vaccine booster are needed to overcome age-related changes in immunity, thus enhancing the health of the elderly.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.025 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".