Anti‐vascular endothelial growth factor therapy and retinal non‐perfusion in diabetic retinopathy: A meta‐analysis of randomised trials
Bibliographic record
Abstract
Abstract Purpose Retinal non‐perfusion (RNP) is fundamental to disease onset and progression in diabetic retinopathy (DR). Whether anti‐vascular endothelial growth factor (anti‐VEGF) therapy can modify RNP progression is unclear. This investigation quantified the impact of anti‐VEGF therapy on RNP progression compared with laser or sham at 12 months. Methods A systematic review and meta‐analysis of randomised controlled trials (RCTs) were performed; Ovid MEDLINE, EMBASE and CENTRAL were searched from inception to 4th March 2022. The change in any continuous measure of RNP at 12 months and 24 months was the primary and secondary outcomes, respectively. Outcomes were reported utilising standardised mean differences (SMD). The Cochrane Risk of Bias Tool version‐2 and the Grading of Recommendations Assessment, Development and Evaluation (GRADE) guidelines informed risk of bias and certainty of evidence assessments. Results Six RCTs (1296 eyes) and three RCTs (1131 eyes) were included at 12 and 24 months, respectively. Meta‐analysis demonstrated that RNP progression may be slowed with anti‐VEGF therapy compared with laser/sham at 12 months (SMD: −0.17; 95% confidence interval [CI]: −0.29, −0.06; p = 0.003; I2 = 0; GRADE rating: LOW) and 24‐months (SMD: −0.21; 95% CI: −0.37, −0.05; p = 0.009; I2 = 28%; GRADE rating: LOW). The certainty of evidence was downgraded due to indirectness and due to imprecision. Conclusion Anti‐VEGF treatment may slightly impact the pathophysiologic process of progressive RNP in DR. The dosing regimen and the absence of diabetic macular edema may impact this potential effect. Future trials are needed to increase the precision of the effect and inform the association between RNP progression and clinically important events. PROSPERO Registration CRD42022314418.
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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 | 0.025 | 0.051 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.053 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".