Macular Perfusional Findings in Venous Obstructive Disease and Its Treatment: An OCT-A Evaluation
Bibliographic record
Abstract
The human retina is supplied by an extensive network of capillaries, where healthy blood flow to various parts of the retina, particularly the macula, is vital for visual functions. Any obstruction in blood flow, known as retinal vein occlusion (RVO), can reduce venous blood return. RVO can occur either at a central location (called central retinal vein occlusion [CRVO]) or a peripheral location (branch vein occlusion [BRVO]). Various techniques have been used to investigate blood flow to the retina and analyze different factors that may impact retinal blood flow. Optical coherence tomographic angiography (OCT-A) has emerged as one of the best methods, with several studies demonstrating its use to investigate changes in blood perfusion status, hemorrhage from blood vessels, and the presence of edema. Some studies have demonstrated that OCT-A is superior to other techniques.<br>Macular edema secondary to RVO is the most common complication that may affect visual acuity and lead to vision loss if left untreated. Several qualitative and quantitative changes caused by RVO can be detected using OCT-A, including vascular blood perfusion and vascular density. Several treatment options have been used to treat macular edema secondary to RVO and other complications. Laser photocoagulation therapy has been used extensively in the past with mixed outcomes. Glucocorticoids, especially dexamethasone (Ozurdex®), have also been used to treat macular edema secondary to RVO. Currently, anti-vascular endothelial growth factor (VEGF) agents are the gold standard for treating RVO. Ranibizumab and aflibercept are approved for the treatment of macular edema secondary to RVO, with faricimab expected to soon be approved.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".