Guidance for anti-VEGF intravitreal injections during the COVID-19 pandemic
Bibliographic record
Abstract
PURPOSE: There is an urgent need to address how to best provide ophthalmic care for patients with retinal disease receiving intravitreal injections with anti-vascular endothelial growth factor agents during the ongoing global COVID-19 pandemic. This article provides guidance for ophthalmologists on how to deliver the best possible care for patients while minimizing the risk of infection. METHODS: The Vision Academy's Steering Committee of international retinal disease experts convened to discuss key considerations for managing patients with retinal disease during the COVID-19 pandemic. After reviewing the existing literature on the issue, members put forward recommendations that were systematically refined and voted on to develop this guidance. RESULTS: The considerations focus on the implementation of steps to minimize the exposure of patients and healthcare staff to COVID-19. These include the use of personal protective equipment, adherence to scrupulous hygiene and disinfection protocols, pre-screening to identify symptomatic patients, and reducing the number of people in waiting rooms. Other important measures include triaging of patients to identify those at the greatest risk of irreversible vision loss and prioritization of treatment visits over monitoring visits where possible. In order to limit patient exposure, ophthalmologists should refrain from using treatment regimens that require frequent monitoring. CONCLUSION: Management of patients with retinal disease receiving intravitreal injections during the COVID-19 pandemic will require adjustment to regular clinical practice to minimize the risk of exposure of patients and healthcare staff, and to prioritize those with the greatest medical need. The safety of patients and healthcare staff should be of paramount importance in all decision-making.
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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 | 0.023 | 0.078 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.024 | 0.014 |
| Insufficient payload (model declined to judge) | 0.018 | 0.011 |
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".