Incidence and Association of Uveitis with COVID-19 Vaccination: A Systematic Review and Meta-Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE: 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. METHODS: 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. RESULTS: = 0.12) from four studies. The evidence quality was rated very low due to the limited number of studies and imprecision. CONCLUSION: 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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.012 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it