Predicting treatment response in retinal vein occlusions using baseline optical coherence tomography biomarkers: A systematic review
Bibliographic record
Abstract
This systematic review examines the prognostic value of baseline optical coherence tomography (OCT) biomarkers in predicting visual acuity (VA) outcomes for eyes with macular edema secondary to retinal vein occlusions (RVO) treated with anti-VEGF therapies, steroids, laser photocoagulation, or combination treatments. VA predictions at 6, 12, and 24 months post-treatment were assessed using a narrative synthesis approach and vote counting based on effect direction relative to a minimal clinically important difference. Certainty of evidence was evaluated using GRADE guidelines. Confounding factors, biomarker variability, and inconsistent outcome measurements were critically analyzed. A total of 116 studies assessing 31 unique OCT biomarkers were included. 'Low' certainty evidence indicated that an intact external limiting membrane (ELM) at baseline predicted better VA at 6 months, while baseline ellipsoid zone (EZ) integrity predicted better VA at 12 months at 5-letter change, however, these associations were not observed at thresholds of 10 and 15 letters. Certainty of evidence was often downgraded due to confounding factors, variability in biomarker definitions, and inconsistent outcomes. These findings highlight OCT biomarkers' potential for prognostication in RVO patients, but underscore the need for standardized definitions and further research to address confounders, improving the reliability and clinical utility of OCT-based biomarkers.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".