Association between COVID-19 vaccine immunogenicity and protection against infection and severe disease in clinically vulnerable patient populations: a systematic review and meta-analysis of observational studies
Bibliographic record
Abstract
BACKGROUND: The use of measured immune responses in informing risk of breakthrough COVID-19 infection and infection outcomes after vaccination against SARS-CoV-2 in clinically vulnerable patients has not been applied clinically. OBJECTIVES: The aim of this study was to investigate the association between measured vaccine immunogenicity and vaccine effectiveness in clinically vulnerable populations. DATA SOURCES: PubMed, MEDLINE, EMBASE, and Cochrane Library. STUDY ELIGIBILITY CRITERIA: Studies published between March 2020 and January 2025, which reported data on COVID-19 vaccine immunogenicity (antibody and T-cell) and subsequent infection outcomes. PARTICIPANTS: Patients defined as clinically vulnerable by QCOVID criteria, who had received at least the primary course of COVID-19 vaccination. ASSESSMENT OF RISK OF BIAS: The Newcastle-Ottawa Quality Assessment Scale was used to assess the risk of bias. METHODS OF DATA SYNTHESIS: A random effects meta-analysis model was used to pool relative risks of COVID-19 breakthrough infection (BTI), hospitalization, and death. Unadjusted data were used for the primary analysis due to the lack of adjusted data available in individual studies. RESULTS: = 65.99). Using the Newcastle-Ottawa Quality Assessment Scale, 5 (11%) studies were of good quality, 2 (7%) of fair quality, and 37 (82%) of poor quality. CONCLUSIONS: Within the methodological limitations, this study has shown that lack of antispike antibody responses was associated with BTI and severe infection outcomes in clinically vulnerable populations. Further research is required to investigate the current utility of testing to inform the ongoing management of clinically vulnerable persons, such as vaccine booster schedules.
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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 | 0.029 | 0.077 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.035 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".