“Vision Loss” and COVID-19 Infection: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Visual impairment in terms of reduced visual acuity and "visual loss" has been reported as an atypical symptom in patients with severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection. This systematic review and meta-analysis aims to assess the cumulative incidence of "visual loss" during coronavirus disease 2019 (COVID-19) and review the current evidence regarding "visual loss" caused by SARS-CoV-2 infection. METHODS: We performed a systematic review and meta-analysis of studies following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We systematically searched the PubMed, Embase, and Scopus databases for relevant studies published that clearly described "vision loss" and SARS-CoV-2 infection. All studies reporting concomitant "vision loss" and laboratory-confirmed SARS-CoV-2 infection were included. Meta-analyses were conducted using the measurement of risk and a 95% confidence interval for each study. RESULTS: Our search identified 1143 manuscripts published in the English language. After study screening, twenty-nine articles were selected: two cross-sectional studies, twenty-four case reports, and three case series. A random-effect meta-analysis demonstrated that the pooled "visual loss" cumulative incidence in COVID-19 patients was 0.16 (95% CI 0.12-0.21). The quality rating of the cross-sectional studies averaged four out of the maximum score on the Newcastle-Ottawa scale. CONCLUSIONS: COVID-19 infection might cause "visual loss". Even if the current evidence is limited, ophthalmological assessment should be promptly provided to all patients experiencing visual impairment symptoms during SARS-CoV-2 infection.
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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 | 0.016 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.043 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".