Anti-VEGF Monotherapy vs Anti-VEGF and Steroid Combination Therapy for Diabetic Macular Edema: A Meta-analysis
Bibliographic record
Abstract
Purpose: To compare the safety and efficacy of antivascular endothelial growth factor (anti-VEGF) monotherapy vs anti-VEGF and steroid combination therapy in treatment-naïve and treatment-resistant patients with diabetic macular edema (DME). Methods: A systematic literature search was conducted from January 2005 to December 2022. Sixteen randomized control trials (RCTs) published in English that reported the efficacy or safety of monotherapy and combination therapy in patients with DME were included. Results: The 16 RCTs included 1166 eyes. Monotherapy was associated with a significantly better best-corrected visual acuity (BCVA) at the final follow-up (weighted mean difference [WMD], −0.04 logMAR; 95% CI, −0.07 to −0.02; P = .002; I 2 = 0%). No significant differences were observed in the change in BCVA between groups at the final observation. Monotherapy was associated with a significantly smaller change in retinal thickness at the final follow-up (WMD, 37.63 μm; 95% CI, 11.67-63.60; P = .005; I 2 = 78%) and with a significantly lower risk for intraocular pressure–related adverse events (AEs) (risk ratio, 0.27; 95% CI, 0.15-0.46; P ≤ .001; I 2 = 0%). The risk for cataract-related AEs was not significantly different between groups ( P = .06). The results in treatment-naïve patients were similar. In treatment-resistant patients, the change in retinal thickness at the final follow-up was similar between groups ( P = .14), but the risk for cataract-related AEs was significantly lower in the monotherapy group in 2 RCTs (risk ratio, 0.09; 95% CI, 0.01-0.66; P = .02; I 2 = 0%). Conclusions: The changes in BCVA were similar despite combination therapy being associated with greater changes in retinal thickness. However, increased complications were seen with combination therapy. Most results in treatment-naïve patients and treatment-resistant patients were similar.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.039 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".