Anti-VEGF intravitreal injections in the era of COVID-19: responding to different levels of epidemic pressure
Bibliographic record
Abstract
Abstract Purpose Following the first wave of the COVID-19 pandemic in early 2020, the easing of strict measures to reduce its spread has led to a resurgence of cases in many countries at both the national and local level. This article addresses how guidance for ophthalmologists on managing patients with retinal disease receiving intravitreal injections of anti-vascular endothelial growth factor (VEGF) during the pandemic should be adapted to the local epidemic pressure, with more or less stringent measures implemented according to the ebb and flow of the pandemic. Methods The Vision Academy's membership of international retinal disease experts analyzed guidance for anti-VEGF intravitreal injections during the COVID-19 pandemic and graded the recommendations according to three levels of increasing epidemic pressure. The revised recommendations were discussed, refined, and voted on by the 14-member Vision Academy Steering Committee for consensus. Results Protocols to minimize the exposure of patients and healthcare staff to COVID-19, including use of personal protective equipment, physical distancing, and hygiene measures, should be routinely implemented and intensified according to local infection rates and pressure on the hospital/clinic or healthcare system. In areas with many COVID-19-positive clusters, additional measures including pre-screening of patients, postponement of non-urgent appointments, and simplification of complex intravitreal anti-VEGF regimens should be considered. Treatment prioritization for those at greatest risk of irreversible vision loss should be implemented in areas where COVID-19 cases are increasing exponentially and healthcare resources are strained. Conclusion Consistency in monitoring of local infection rates and adjustment of clinical practice accordingly will be required as we move forward through the COVID-19 era. Ophthalmologists must continue to carefully weigh the risk–benefits to minimize the exposure of patients and healthcare staff to COVID-19, ensure that patients receive sight-saving treatment, and avoid the potential long-term impact of prolonged treatment postponement.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".