Risk of Corneal Transplant Rejection Following COVID-19 Vaccination: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
The COVID-19 pandemic has initiated an unparalleled global vaccination campaign, raising concerns about the vaccine's effects on various health conditions, including the risk of corneal transplant rejection. This systematic review aimed to identify the relationship between COVID-19 vaccination and rejection of corneal transplant, filling a significant gap in the existing medical literature. A literature search was performed across multiple databases up to February 12, 2024, to identify studies evaluating the risk of corneal transplant rejection post-COVID-19 vaccination. Eligible studies were original research that reported outcomes of corneal graft rejection following vaccination. Nested Knowledge web software facilitated screening and data extraction. The Newcastle–Ottawa Scale was employed for quality assessment. A meta-analysis was conducted to calculate the aggregated relative risk (RR) utilizing R software version 4.3. Six studies were included in the qualitative synthesis, with four meeting the criteria for meta-analysis. These studies varied in geographic location, surgical techniques, and types of vaccines used. The pooled RR for corneal transplant rejection following COVID-19 vaccination was 0.816 (95% CI 0.178–1.453), indicating no significant risk of rejection. No statistical heterogeneity was observed among the studies ( I 2 = 0%). This review and meta-analysis found no significant evidence that COVID-19 vaccination increases the risk of corneal graft rejection. However, the current evidence is insufficient to conclusively determine the vaccine's safety for corneal transplant recipients. These findings underscore the need for additional research to confirm these preliminary results and investigate the long-term effects of COVID-19 vaccination on corneal transplants, aiming to provide evidence-based guidance to healthcare providers and patients.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".