Evaluation of contrast sensitivity and other visual function outcomes in diabetic macular edema patients following treatment switch to aflibercept from ranibizumab
Bibliographic record
Abstract
Purpose: This study aims to investigate changes in contrast sensitivity (CS), visual acuity (VA), central retinal thickness (CRT), and vision-related quality of life in subjects with recalcitrant diabetic macular edema switched from long-term ranibizumab treatment to aflibercept. Patients and methods: In this prospective, investigator-masked, single-center study, 40 patients with persistent fluid, despite previous ranibizumab treatment, were switched to aflibercept with 5 consecutive monthly doses. The primary outcome was mean change from baseline to week 20 in Pelli–Robson CS. Secondary outcomes were mean change from baseline in best-corrected VA (BCVA), CRT, and National Eye Institute 25-Item Visual Function Questionnaire score. Results: Fifty eyes (baseline VA >6/30) were evaluated. A median of 21.1±11.9 (range 5–55) ranibizumab injections were administered prior to initiation of aflibercept. Mean CS improved from 1.40±0.14 log units at baseline to 1.46±0.15 log units at week 20 ( P <0.001). VA improved with mean logarithm of the minimum angle of resolution BCVA of 0.33±0.19 at baseline compared with logarithm of the minimum angle of resolution BCVA of 0.28±0.16 at week 20 ( P =0.0016). Mean CRT decreased from 324±85 to 289±61 µm ( P <0.001). Twenty-two (55%) patients experienced an overall improvement in National Eye Institute 25-Item Visual Function Questionnaire score. Interestingly, an association was found between changes in CS and change in CRT ( r 2 =0.385, P <0.001) and between changes in BCVA and change in CRT ( r 2 =0.092, P =0.032). Conclusion: Switching from ranibizumab to aflibercept in patients with recalcitrant diabetic macular edema resulted in an improvement in all measured metrics, including CS, VA, and CRT. A majority of patients also indicated an improvement in vision-related quality of life. The finding of a stronger relationship between changes in CRT and CS compared with changes in CRT and BCVA suggests that the inclusion of CS as an endpoint may yield a more complete understanding of visual outcomes than that obtained by using VA alone. Keywords: aflibercept, ranibizumab, diabetic macular edema, contrast sensitivity, visual acuity, anti-VEGF
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 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".