The Association between Retinal Thickness Fluctuations and Visual Outcomes under Anti-Vascular Endothelial Growth Factor Therapy: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
INTRODUCTION: The objective of this study was to examine the association between retinal thickness (RT) fluctuations and best corrected visual acuity (BCVA) in eyes with neovascular AMD, macular edema secondary to RVO, and DME treated with anti-VEGF therapy. METHODS: A systematic search of Ovid MEDLINE, EMBASE, and the Cochrane Library was performed from January 2006 to March 2024. Studies comparing visual or anatomic outcomes of patients treated with anti-VEGF therapy, stratified by magnitudes of RT fluctuation, were included. ROBINS-I and Cochrane RoB 2 tools were used to assess risk of bias, and certainty of evidence was evaluated with GRADE criteria. Meta-analysis was performed with a random-effects model. Primary outcomes were final BCVA and change in BCVA relative to baseline. RESULTS: 15,725 articles were screened; 15 studies were identified in the systematic review and 5 studies were included in the meta-analysis. Final ETDRS VA was significantly worse in eyes with the highest level of RT fluctuation (weighted mean difference [WMD] = 7.86 letters; 95% CI, 4.97, 10.74; p < 0.00001; I2 = 81%; 3,136 eyes). RT at last observation was significantly greater in eyes with high RT fluctuations (WMD = -27.35 μm; 95% CI, -0.04, 54.75; p = 0.05; I2 = 88%; 962 eyes). CONCLUSIONS: Final visual outcome is associated with magnitude of RT fluctuation over the course of therapy. It is unclear whether minimizing RT fluctuations would help optimize visual outcomes in patients treated with anti-VEGF therapy. These findings are limited by a small set of studies, risk of bias, and considerable heterogeneity.
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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 | 0.017 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.035 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".