Outcomes of Second-Line Intravitreal Anti-VEGF Switch Therapy in Radiation Retinopathy Secondary to Uveal Melanoma: Moving from Bevacizumab to Aflibercept
Bibliographic record
Abstract
Introduction: Radiation retinopathy is a dose-dependent complication of the retina following exposure to ionizing radiation. The objective of this prospective case series is to determine the clinical efficacy of intravitreal aflibercept for radiation retinopathy secondary to radiotherapy for uveal melanoma in those that failed intravitreal bevacizumab treatment. Methods: A case series of 30 patients with a mean age of 57 ± 15 years with radiation retinopathy were enrolled. Visual acuity (VA) and central foveal thickness (CFT) responses to therapy were assessed with regression analyses at 1 month, 3 months, and 6 months following the switch to aflibercept. Results: Regression analyses showed a statistically significant reduction in CFT and improvements in VA following the switch to treatment by aflibercept at 1 month, 3 months, and 6 months. The mean CFT improved from 476 μm ± 170 to 386 μm ± 139 and the mean VA improved minimally from 20/115 ± 20/63 to 20/112 ± 20/54 over 6 months. After 6 months of aflibercept, 46% of patients displayed a CFT improvement of 100 μm or greater and 23% of patients showed improvement in VA of 1 line or better. Conclusion: This pilot study suggests that patients with radiation retinopathy who have failed monthly intravitreal bevacizumab may respond to aflibercept.
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.
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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".