A Systematic Review of Clinical Studies Using VEGF Inhibitors in the Treatment of Macular Edema from Diabetic Retinopathy
Bibliographic record
Abstract
Purpose: To synthesize the present clinical evidence of efficacy and adverse events of commonly used anti-VEGF drugs for Diabetic Macular Edema. Methods: A systematic review was undertaken from the Medline, Biosis, CINAHL, Cochrane and Web of Science databases. Grey literature that consisted of lectures, seminars and conferences was also retrieved. The cut-off date was January 1 2014. A two-stage screening process was undertaken followed by a data extraction stage using the systematic review software EPPI. These were done by two reviewers. Heterogeneous meta-analysis was performed on the primary outcome which was change in macular thickness from baseline after injection. Side effects were tabulated. Results: From 846 articles that were initially screened, 18 papers were included in the data extraction stage. For all anti-VEGF treatments, the average decrease in macular thickness was 114.4 microns (95% CI: 66.8 - 162 μM). The average decrease in thickness from Lucentis (161.9 μM) was larger than that for Avastin (96.5 μM) but this was not statistically significant (p = 0.23). The most common complications were vitreous hemorrhage, endophthalmitis and retinal detachment. Vision threatening complications were rare but were reported regularly. Conclusions: The synthesized clinical evidence to date supports both of these treatments as efficacious and safe for diabetic macular edema (DME). There is a trend toward greater efficacy for Lucentis over Avastin but this is not statistically significant and will need a head-to-head RCT to assess accurately.
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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 | 0.059 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.010 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| 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".