Global impact of COVID-19 on corneal donor tissue harvesting and corneal transplantation
Bibliographic record
Abstract
Introduction: The purpose of this review is to consolidate and examine the available literature on the coronavirus disease 2019 pandemic and its effect on corneal transplantation and eye banking. Methods: A primary literature search was conducted using the PubMed (Medline) database with keywords and MeSH terms such as "corneal transplantation," "eye banks," "keratoplasty" and then were combined with COVID-19. Relevant articles through September 2022 were assessed and 25 articles were included in this review. Results: Donor tissue volumes declined globally during lockdown periods due to a lower number of referrals and tighter tissue screening guidelines. Rates of elective surgeries decreased in the lockdown period compared to respective periods in previous years. However, changes in rates of emergency procedures were not uniform across different regions. Moreover, rates of different elective corneal grafts [i.e., penetrating keratoplasty (PK), endothelial keratoplasty (EK), or anterior lamellar keratoplasty (ALK)] were affected differently with the pattern of change being dependent on region-specific factors. Conclusion: Both donor tissue volumes and rates of corneal transplant procedures were affected by lockdown restrictions. The underlying etiology of these changes differed by region. Examining the range of impact across many countries as well as the contributing factors involved will provide guidance for future global pandemics.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".