Visual Function for Driving in Diabetic Macular Edema and Retinal Vein Occlusion Post-Stabilization with Anti-Vascular Endothelial Growth Factor
Bibliographic record
Abstract
Purpose: What is the level of visual function in patients with diabetic macular edema (DME) and retinal vein occlusion (RVO) post-stabilization with anti-vascular endothelial growth factor? Patients and Methods: This observational non-controlled single center study evaluated visual function in two patient populations with macular edema 25 with diabetic macular edema and 25 with retinal vein occlusion treated following standard protocol of anti-VEGF therapy post- stabilization. Results: A total of 68 eyes from 50 patients were analyzed including 18 bilateral and 7 unilateral diabetic macular edema, 14 patients with central and 11 with branch retinal vein occlusion. The mean age was 69± 11 years and 64% were male. In the RVO group: LogMAR BCVA was 0.12± 0.13 compared to the unaffected eye 0.04± 0.05 (P=< 0.01), contrast sensitivity in the treated eye was 1.69± 0.21 log units compared to 1.84± 0.15 log units in the unaffected eye (p=< 0.01), the ganglion cell volume was 0.88± 0.15 mm 3 in the treated eye compared to 1.04± 0.1 mm 3 in the unaffected eye (P=< 0.01). In the diabetic macular edema group: LogMAR BCVA was 0.17± 0.13, contrast sensitivity in the treated eye was 1.16± 0.21 log units compared to the normal population 1.92± 0.8 log units (p=< 0.01), the ganglion cell volume was 0.94± 0.14 mm 3 in the treated eye compared to 1.03± 0.12 mm 3 in the normal population (P=< 0.001). In both groups a majority of treated eyes retained visual acuity ≥+0.4 LogMAR (diabetic macular edema 95%, RVO 96%) however contrast sensitivity was more than two standard deviations below the normal population mean in a majority of treated eyes in both groups (diabetic macular edema 88% RVO 64%). Conclusion: Impairment in contrast sensitivity in both groups could impact activities of daily living including driving and should prompt questions about how we advise patients regarding their level of function and the potential limitations/restrictions that should be placed on such activities. Keywords: contrast sensitivity, residual deficit, functional impairment
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".