Safety and Efficacy of a Treat-and-Extend Regimen of Anti–Vascular Endothelial Growth Factor Agents for Diabetic Macular Edema or Macular Edema Secondary to Retinal Vein Occlusion: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
This article has been amended to include factual corrections. An error was identified subsequent to print publication. Figure 2 was incorrect, and has been revised and updated. The online article is considered the version of record. Background and Objective: This meta-analysis evaluates treat-and-extend regimens relative to monthly and as-needed (prn) regimens using anti–vascular endothelial growth factor agents for diabetic macular edema and macular edema secondary to retinal vein occlusion. Materials and Methods: Comparative studies evaluating a treat-and-extend regimen relative to a monthly or prn regimen with anti–vascular endothelial growth factor therapy for diabetic macular edema or macular edema secondary to retinal vein occlusion were included following a systematic literature search. Results: Seven studies of 984 eyes were included. Relative to a monthly regimen, treat-and-extend was similar for change in best-corrected visual acuity at final follow-up ( P = .59) and had a lower number of injections ( P < .00001). Relative to a prn regimen, treat-and-extend was similar for change in best-corrected visual acuity at final follow-up ( P = .84) and was associated with a higher number of injections ( P = .02). Conclusion: This meta-analysis found that a treat-and extend regimen was nonsignificantly different compared to monthly and prn regimens in efficacy and safety end points. [ Ophthalmic Surg Lasers Imaging Retina 2023;54(3):131–138.]
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".