One-year outcomes of Aflibercept for refractory diabetic macular edema in Bevacizumab nonresponders
Bibliographic record
Abstract
Purpose: A sub-population of patients with diabetic macular edema (DME) responds less effectively to off-label use of Bevacizumab. Approval of Aflibercept for DME has offered Bevacizumab nonresponders an alternative therapeutic option. Herein, we investigate the anatomical and functional changes associated with Aflibercept treatment in Bevacizumab nonresponders with chronic DME in a Canadian setting. Methods: A retrospective study of eyes with persistent DME that were switched to Aflibercept due to nonresponse following ≥6 consecutive monthly Bevacizumab injections was performed. Anatomical and functional changes and the predictors of response were assessed using patients' characteristics prior to receiving their first (baseline) and seventh consecutive Aflibercept injections (follow-up). Results: Twenty-four eyes were included, with a mean age of 63.9 ± 10.7 years, an average of 16.8 ± 8.5 Bevacizumab injections prior to switching to Aflibercept, and mean follow-up duration of 11.8 ± 1.7 months following switching to Aflibercept. Best-corrected visual acuity (BCVA) improved significantly from 0.49 ± 0.13 to 0.41 ± 0.11 logMAR (P < 0.001) and central subfield thickness (CST) decreased by 119.4 μm from 409.4 ± 85.8 μm to 290.0 ± 64.5 μm (P < 0.001), with 50% of eyes showing complete anatomical response. Worse BCVA and higher CST at baseline predicted greater vision improvements (P = 0.001 and P = 0.035, respectively) while a larger decrease in CST was associated with greater baseline CST (P = 0.001) and better glycemic control (P = 0.039). Conclusion: Our data from a real-world clinical setting highlight the efficacy of Aflibercept as an alternative therapeutic option for DME recalcitrant to Bevacizumab, with potential additional benefit to those with worse vision, greater CST, and better glycemic control at baseline.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".