<p>Two-year analysis of changes in the optic nerve and retina following anti-VEGF treatments in diabetic macular edema patients</p>
Bibliographic record
Abstract
Purpose: To evaluate long-term structural and functional changes that happen to the optic nerve and retina following ranibizumab (Lucentis) injections in diabetic macular edema (DME) patients. Methods: Patients with clinically significant DME requiring anti-VEGF injections underwent pre-injection baseline, 6, 12, and 24 month follow-up tests. The tests performed were optical coherence tomography (OCT), best-corrected visual acuity (BCVA), and visual field (VF). Wide-field fluorescein angiogram (IVFA) was performed to monitor the progression of diabetic ischemia. Results: A total of 30 patients requiring anti-VEGF injections and 21 control patients not requiring anti-VEGF injections were enrolled in the study. From baseline, the average macular thickness significantly decreased ( p <0.0002) over the 24-month time period. Mean perfused ratio significantly increased ( p <0.0005) at 6, 12, and 24 months. Cup volume and vertical cup-to-disk ratio significantly increased ( p <0.0014) over the study period. This was verified by masked independent grading of patient optic nerve stereo-photographs by glaucoma specialists. BCVA significantly ( p <0.0006) improved over the study period. VFs showed a non-significant trend of deteriorating peripheral vision at 12 and 24 months. Conclusion: Clinically, anti-VEGF therapy appears to affect the optic nerve by increasing cup volume and increasing vertical cup/disk ratio over time. The results provide a cautionary note to monitor both the retina and optic nerve status in patients undergoing frequent injections. Keywords: diabetic macular edema, retina, optic nerve, anti-VEGF, lucentis, ranibizumab
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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".