Relationship Between Macular Thickness and Visual Acuity in the Treatment of Diabetic Macular Edema With Anti-VEGF Therapy: Systematic Review
Bibliographic record
Abstract
Purpose: To examine the relationship between central macular thickness (CMT) measured by optical coherence tomography (OCT) and visual acuity (VA) in patients with center-involving diabetic macular edema (DME) receiving antivascular endothelial growth factor (anti-VEGF) treatment. Methods: Peer-reviewed articles from 2016 to 2020 reporting intravitreal injections of bevacizumab, ranibizumab, or aflibercept that provided data on pretreatment (baseline) and final retinal thickness (CMT) and visual acuity (VA) were identified. The relationship between relative changes was assessed via a linear random-effects regression model controlling for treatment group. Results: No significant association between the logarithm of the minimum angle of resolution (logMAR) VA and CMT was found in 41 eligible studies evaluating 2667 eyes. The observed effect estimate was a 0.12 increase (95% CI, -0.124 to 2.47) in logMAR VA per 100 µm reduction in CMT after treatment change. There were no significant differences in logMAR VA between the anti-VEGF treatment groups. Conclusions: There was no statistically significant relationship between the change in logMAR VA and change in CMT as well as no significant effect of the type of anti-VEGF treatment on the change in logMAR VA. Although OCT analysis, including measurements of CMT, will continue to be an integral part of the management of DME, further exploration is needed on additional anatomic factors that might contribute to visual outcomes.
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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 | 0.006 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".