Optimal Treatment of Retinal Vein Occlusion: Canadian Expert Consensus
Bibliographic record
Abstract
BACKGROUND: The availability of new therapeutic approaches, particularly intravitreal anti-vascular endothelial growth factor (anti-VEGF) therapies, has prompted significant changes to the established treatment paradigms for retinal vein occlusion (RVO). Better visual outcomes and significantly lower rates of adverse events have been noted in multiple large randomized clinical trials and have led to a new standard of care for this sight-threatening condition. OBJECTIVE: To develop an expert consensus for the management of RVO and associated complications in the context of recent clinical evidence. METHODS: The development of a Canadian expert consensus for optimal treatment began with a review of clinical evidence, daily practice, and existing treatment guidelines and algorithms. The expert clinicians (11 Canadian retina specialists) met on February 1, 2014, in Toronto to discuss their findings and to propose strategies for consensus. RESULTS: The result of this expert panel is a consensus proposal for Canadian ophthalmologists and retina specialists treating patients presenting with RVO. Treatment algorithms specific to branch and central RVO (BRVO and CRVO) were also developed. CONCLUSIONS: The consensus provides guidelines to aid clinicians in managing RVO and associated complications in their daily practice. In summary, laser remains the therapy of choice when neovascularization secondary to RVO is detected. Adjunctive anti-VEGF could be considered in managing neovascularization secondary to RVO in cases of vitreous hemorrhage. Intravitreal anti-VEGF should be considered for symptomatic visual loss associated with center-involving macular edema on optical coherence tomography. Patients with BRVO and a suboptimal response to anti-VEGF could be treated with grid laser, and those with CRVO and an inadequate response to anti-VEGF may be candidates for intravitreal steroids.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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 teacher head, 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".