Treat‐and‐extend regimens of anti‐vascular endothelial growth factor therapy for retinal vein occlusions: a systematic review and meta‐analysis
Bibliographic record
Abstract
OBJECTIVE: To investigate treat-and-extend (T&E) regimens of anti-vascular endothelial growth factor (anti-VEGF) therapy for the treatment of macular oedema secondary to retinal vein occlusions (RVOs). METHODS: Ovid MEDLINE, Ovid EMBASE and CENTRAL were searched on 25 February 2021. Randomized controlled trials, cohort studies, case-control studies and case series were included. The primary outcome was the change in Early Treatment Diabetic Retinopathy Score (ETDRS) letters from baseline. Conversions from Snellen to ETDRS letters were performed utilizing a published protocol. Secondary outcomes included improvement in retinal thickness from baseline, number of anti-VEGF injections and frequency of adverse events. Outcomes were examined at 12 and 24 months. Certainty of evidence was assessed utilizing GRADE (Grading of Recommendations Assessments, Development and Evaluations) guidelines. RESULTS: Seven hundred eighty-six eyes from 16 studies were included. Meta-analysis demonstrated a mean improvement of 15.7 (95% CI: 13.3-18.0) ETDRS letters at 12 months. Central retinal thickness improved 269.7 μm (95% CI: 233.64-305.90) at 12 months. Injections were performed 8.1 (95% CI: 7.4-8.7) and 13.1 (95% CI: 9.4-16.8) times at 12 and 24 months respectively. Adverse events were infrequent across all studies. Grading of Recommendations Assessments, Development and Evaluations (GRADE) certainty of evidence was very low across all outcomes. CONCLUSIONS: The results support the viability of T&E regimens for the treatment of macular oedema secondary to RVOs.
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.022 | 0.033 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".