Anti-Vascular Endothelial Growth Factor Treatment Compared with Steroid Treatment for Retinal Vein Occlusion: A Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Intravitreal anti-vascular endothelial growth factor (anti-VEGF) and steroid treatment are both used for macular edema (ME) secondary to retinal vein occlusion (RVO), however a continual reevaluation of their comparative efficacy is required. OBJECTIVES: This meta-analysis aimed to compare the efficacy and safety of intravitreal anti-VEGF agents and intravitreal steroids for the treatment of ME secondary to RVO. METHODS: A systematic literature search was conducted on Ovid MEDLINE, EMBASE, and the Cochrane Controlled Register of Trials for studies published between January 2005 and November 2021. Randomized controlled trials (RCTs) reporting on patients with ME secondary to RVO who were treated with intravitreal steroids or anti-VEGF agents were included. A random effects meta-analysis was performed. RESULTS: 879 eyes from 11 RCTs were included. At the last study observation, intravitreal anti-VEGF agents were associated with a significantly better best corrected visual acuity (WMD = -0.14 logMAR, 95% CI = [-0.19, -0.09], p < 0.00001) and lower retinal thickness (WMD = -38.01 µm, 95% CI = [-56.17, -19.85], p < 0.0001) relative to intravitreal steroids. Similar findings were found at 3-12 month time points. Intravitreal anti-VEGF agents were associated with a significantly lower incidence of IOP-related adverse events (RR = 0.28, 95% CI = [0.15, 0.51], p < 0.0001), cataract development/progression (RR = 0.22, 95% CI = [0.09, 0.49], p = 0.0003), and conjunctival hemorrhage (RR = 0.52, 95% CI = [0.32, 0.86], p = 0.01). CONCLUSIONS: Our meta-analysis found superiority of intravitreal anti-VEGF agents relative to intravitreal steroids for the treatment of ME secondary to RVO with regards to visual acuity, anatomic outcomes, and safety endpoints.
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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 | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.054 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".