Updates on medical and surgical managements of diabetic retinopathy and maculopathy
Bibliographic record
Abstract
Diabetic retinopathy (DR) and diabetic macular edema (DME) are leading causes of vision loss globally. This is a comprehensive review focused on both medical and surgical management strategies for DR and DME. This review highlights the epidemiology of DR and DME, with a particular emphasis on the Asia-Pacific region, urban-rural disparities, ethnic variations, and grading methodologies. We examine various risk factors for DR, including glycemic control, hypertension, hyperlipidemia, obesity, chronic kidney disease, sex, myopia, pregnancy, and cataract surgery. Furthermore, we explore potential biomarkers in serum, proteomics, metabolomics, vitreous, microRNA, and genetics that may aid in the detection and management of DR. In addition to medical management, we review the evidence supporting systemic and ocular treatments for DR/DME, including anti-vascular endothelial growth factor (anti-VEGF) agents, anti-inflammatory agents, biosimilars, and integrin inhibitors. Despite advancements in treatment options such as pan-retinal photocoagulation and anti-VEGF agents, a subset of cases still progresses, necessitating vitrectomy. Challenging diabetic vitrectomies pose difficulties due to complex fibrovascular proliferations, incomplete posterior vitreous detachment, and fragile, ischemic retinas, making membrane dissection risky and potentially damaging to the retina. In this review, we address the question of challenging diabetic vitrectomies, providing insights and strategies to minimize complications. Additionally, we briefly explore newer modalities such as 3-dimensional vitrectomy and intra-operative optical coherence tomography as potential tools in diabetic vitrectomy. In conclusion, this review provides a comprehensive overview of both medical and surgical management options for DR and DME. It underscores the importance of a multidisciplinary approach, tailored to the needs of each patient, to optimize visual outcomes and improve the quality of life for those affected by these sight-threatening conditions.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".