Integrated Assessment of OCT, Multimodal Imaging, and Cytokine Markers for Predicting Treatment Responses in Retinal Vein Occlusion Associated Macular Edema: A Comparative Review of Anti-VEGF and Steroid Therapies
Bibliographic record
Abstract
Retinal vein occlusion (RVO) is a significant cause of vision loss, characterized by the occlusion of retinal veins, leading to conditions such as central retinal vein occlusion (CRVO) and branch retinal vein occlusion (BRVO). Macular edema (ME), a prevalent consequence of RVO, is the primary cause of vision impairment in affected patients. Anti-VEGF agents have become the standard treatment, showing efficacy in improving visual acuity (VA) and reducing ME. However, a subset of patients exhibit a suboptimal response to anti-VEGF therapy, necessitating alternative treatments. Corticosteroids, which address inflammatory pathways implicated in ME, have shown promise, particularly in cases resistant to anti-VEGF. This review aims to identify biomarkers that predict treatment response to corticosteroids in RVO-associated ME, utilizing multimodal imaging and cytokine assessments. Baseline imaging, including SD-OCT and OCT-A, is essential for evaluating biomarkers like hyperreflective foci (HRF), serous retinal detachment (SRF), and central retinal thickness (CRT). Elevated cytokine levels, such as IL-6 and MCP-1, correlate with ME severity and poor anti-VEGF response. Early identification of these biomarkers can guide timely transitions to corticosteroid therapy, potentially enhancing treatment outcomes. The practical conclusion of this review is that integrating biomarker assessment into clinical practice enables personalized treatment decisions, allowing for earlier and more effective management of RVO-associated ME by transitioning patients to corticosteroid therapy when anti-VEGF agents are insufficient. Advanced diagnostics and machine learning may further refine personalized treatment strategies, improving the management of RVO-associated ME.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".