Incidence and Association of Uveitis with COVID-19 Vaccination: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
In the wake of the COVID-19 pandemic, vaccines have been pivotal in curbing disease spread and severity. However, concerns over post-vaccination adverse events, including uveitis, an inflammatory ocular condition, have been noted. This systematic review and meta-analysis aimed to evaluate the incidence and association of uveitis following COVID-19 vaccination. A literature search was performed across several databases on October 21, 2023. Human studies examining the incidence of uveitis post-COVID-19 vaccination were included. The Newcastle-Ottawa Scale was used for quality appraisal of the included studies. Meta-analysis was performed to assess the overall incidence of uveitis and the relative risk of developing the condition post-vaccination. All statistical analyses were performed using R software version 4.3. Six studies involving over 2 billion vaccine doses were included. The overall incidence of uveitis was 0.016% (95% CI: 0.010 to 0.026). No significant association was found between vaccination and the onset of uveitis (Relative Risk: 1.45 (95% CI: 0.82 to 2.57, p = 0.12) from four studies. The evidence quality was rated very low due to the limited number of studies and imprecision. This analysis indicates a low incidence of uveitis following COVID-19 vaccination and no significant association with the vaccine. The findings are constrained by the small number of studies and low certainty of evidence, underscoring the need for further research. Comprehensive and longitudinal studies are necessary to confirm these findings and reinforce public confidence in COVID-19 vaccination programs.
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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 | 0.014 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.042 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".