Impact of COVID-19 on a real-world treat-and-extend regimen with aflibercept for neovascular age-related macular degeneration
Bibliographic record
Abstract
OBJECTIVE: To assess the effect of the COVID-19 pandemic on injection intervals among patients treated for neovascular age-related macular degeneration. DESIGN: Retrospective cohort study. PARTICIPANTS: Patients treated at a single practice using a treat-and-extend regimen with intravitreal aflibercept between December 2018 and April 2021. METHODS: The primary outcome was the change in injection intervals. Secondary outcomes included differences in best-recorded visual acuity (BRVA) and central subfield thickness (CST). Associations were evaluated with linear mixed-effects modelling. RESULTS: This study included 1839 injections from 185 eyes (141 patients). The median (interquartile range) injection intervals in the pre-COVID-19 and COVID-19 periods were 60 (42-70) and 70 (49-90) days, respectively. The pandemic was associated with a mean injection interval lengthening of 7.2 days (P < 0.001), a decrease in BRVA of 3.1 Early Treatment Diabetic Retinopathy Study letters (P < 0.001), and a reduction in CST of 14.7 μm (P = 0.003). The presence of exudative intraretinal fluid was associated with a reduction in treatment intervals of 11.1 days (P < 0.001), a reduction in BRVA of 1.9 Early Treatment Diabetic Retinopathy Study letters (P < 0.001), and an increase in CST of 52.4 μm (P < 0.001). The presence of subretinal fluid was associated with a reduction in treatment intervals of 8.5 days (P < 0.001) and an increase in CST of 21.6 μm (P < 0.001). CONCLUSIONS: This real-world study estimated that the severe acute respiratory syndrome coronavirus 2 pandemic resulted in an injection extension of 7.2 days with associated decreases in BRVA and CST that are unlikely clinically significant on a population basis. This builds on evidence suggesting that long-term vascular endothelial growth factor suppression can facilitate meaningful interval extensions while maintaining visual acuity.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".