Ocular manifestations of COVID-19: A systematic review of current evidence
Bibliographic record
Abstract
Introduction: COVID-19 caused by SARS-CoV-2, commonly presents with symptoms such as fever and shortness of breath but can also affect other organs. There is growing evidence pointing to potential eye complications. In this article, we aim to systematically review the ocular manifestations of COVID-19. Methods: We conducted a systematic review to explore the ocular manifestations of COVID-19. We searched online databases including PubMed, Embase, Scopus, and Web of Science up to September 4, 2023. After a two-stage screening process and applying inclusion/exclusion criteria, eligible articles were advanced to the data extraction phase. The PRISMA checklist and Newcastle-Ottawa Scale (NOS) were used for quality and bias risk assessments. Results: We selected and extracted data from 42 articles. Most of the studies were cross-sectional (n = 33), with the highest number conducted in Turkey (n = 10). The most frequent ocular manifestation was conjunctivitis, reported in 24 articles, followed by photophobia, burning, chemosis, itching, and ocular pain. Most studies reported complete recovery from these manifestations; however, one study mentioned visual loss in two patients. Conclusion: In general, ocular manifestations of COVID-19 appear to resolve either spontaneously or with supportive treatments. For more severe cases, both medical treatment and surgery have been employed, with the outcomes suggesting that complete recoveries are attainable.
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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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".