The impact of COVID-19 on managing ophthalmic diseases: an international, descriptive study
Bibliographic record
Abstract
BACKGROUND: Anecdotally, the COVID-19 pandemic has resulted in more severe cases of eye disease, decreased medication compliance/availability, and decreased treatment volume due to the lockdown. AIMS: We aim to quantify and bring together a variety of international perspectives from ophthalmologists of different subspecialties to characterize the global impact of COVID-19 on managing various ophthalmic disease. METHODS: An online survey of 10 questions was conducted among ophthalmologists using a specialized survey program. RESULTS: Fifty-two ophthalmologists were successfully contacted. Survey respondents include ophthalmologists from USA, Canada, Korea, Mexico, and New Zealand. Based on the results of our survey, 1 year after the pandemic, ophthalmic disease severity has worsened as well as a decrease in examination and medication compliance. CONCLUSIONS: Ophthalmologists across the world have reported a general increase in disease severity and decrease in medication and examination compliance 1 year after the beginning of COVID-19.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".