Ophthalmology-focused publications and findings on COVID-19: A systematic review
Why this work is in the frame
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Bibliographic record
Abstract
PURPOSE: To summarize COVID-19 research endeavors by ophthalmologists/researchers in terms of publication numbers, journals and author countries, and to detail key findings. METHODS: The LitCovid database was systematically reviewed for ophthalmology-focused COVID-19 articles. The quality of the evidence was assessed for articles investigating conjunctivitis in COVID-19 patients. RESULTS: There were 21,364 articles in LitCovid on June 12, 2020, of which 215 (1%) were ophthalmology-focused. Of articles on COVID-19 transmission, 3.3% were ophthalmology-focused. Ophthalmology-focused articles were published in 68 journals and originated from 25 countries. The top five countries publishing ophthalmology-focused articles (China, India, United States of America, Italy, and United Kingdom) produced 145/215 (67%) articles. A total of 16 case reports/series from eight countries reported that conjunctivitis can be the initial or the only symptom of COVID-19 infection. Conjunctivitis may occur in the middle phase of COVID-19 illness. A total of 10 hospital-based cross-sectional studies reported that between 0% and 31.6% of COVID-19 patients have conjunctivitis or other ocular conditions, with a pooled prevalence of 5.5% reported in a meta-analysis. Viral RNA was detected in conjunctival swabs of patients with and without ocular manifestations, after resolution of conjunctivitis, after nasopharyngeal swabs turned negative and in retina of deceased COVID-19 patients. CONCLUSION: Within 3 months of declaring the COVID-19 pandemic, 215 ophthalmology-focused articles were published in PubMed, concentrating on disease manifestations and transmission. The reported presence of conjunctivitis or other ocular conditions in COVID-19 patients is varied. Clinicians should be alert for ocular involvement in COVID-19 infections and possible ocular transmission even in patients without ocular symptoms.
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.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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