Treatment and prophylaxis of radiation optic neuropathy: A systematic review and meta-analysis
Bibliographic record
Abstract
PURPOSE: Radiation optic neuropathy (RON) generally follows radiation therapy that exceed 50 Gy to the visual axis and occurs within three years of therapy. Currently, there are no universally accepted treatments or prophylaxis for RON. The review aimed to examine the efficacy of all treatments and prophylaxis for RON. METHODS: MEDLINE, Embase, the Cochrane Library, and gray literature were searched to December 2020. Studies on treatment(s) and/or prophylaxis of RON were included. Results were meta-analyzed using a random-effects model. Primary outcomes included the proportions of patients who experienced improvement, no change, or worsening of visual acuity (VA) for each treatment. Secondary outcome was the incidence of RON for studies on prophylaxis. RESULTS: Overall, 50 studies (n = 5397) were included. Meta-analysis (n = 1752) showed significantly lower incidence of RON in patients who underwent intravitreal anti-VEGF prophylaxis compared to control (RR 0.64, 95%CI [0.48, 0.86]) for uveal melanoma. Intravitreal anti-VEGF injections (n = 68), hyperbaric oxygen therapy alone (n = 14), and pentoxifylline (n = 5) resulted in improved or stable vision ≤1 logMAR in 54.5%, 42.9%, and 40.0% of patients, respectively. Systemic corticosteroids (n = 82), anticoagulants (n = 12), and systemic bevacizumab (n = 7) showed improved or stable vision ≤1 logMAR in 17.1%, 33.3%, and 14.3% of patients, respectively. Overall risk of bias was low, but evidence was limited to retrospective studies. CONCLUSION: Intravitreal anti-VEGF injections reduced incidence of RON in irradiated uveal melanoma patients. Systemic corticosteroids, systemic bevacizumab, and warfarin alone are likely ineffective treatments. Early hyperbaric oxygen therapy and intravitreal anti-VEGF injections were most effective among those investigated and require further investigation.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.027 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".