Intravitreal anti-vascular endothelial growth factor for the treatment of chronic central serous retinopathy: a meta-analysis of the literature
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to evaluate the role of anti-vascular endothelial growth factor (anti-VEGF) treatment on the functional and structural parameters of chronic central serous retinopathy (CSR). METHODS: PubMed was used to systematically review literature published from 1 January 2009 to 1 July 2022. Studies were included if patients in their cohort had symptoms for more than 3 months, anti-VEGF treatment was provided and the following outcomes were reported: best-corrected visual acuity (BCVA), central macular thickness (CMT) and proportion of subretinal fluid (SRF) resolution. RESULTS: 339 eyes met inclusion criteria with a mean patient age of 45.8±4.9 years. The weighted mean baseline BCVA for the 20 studies was 0.39±0.23 logMAR, which improved to 0.28±0.24 after treatment with anti-VEGF injections (p=0.069). The weighted baseline CMT for the 20 studies decreased from 395.2±52.0 µm to 243.0±41.9 µm (p<0.001). The weighted overall percentage of SRF resolution was 68.4%. CONCLUSION: Anti-VEGF treatment demonstrated significantly decreased macular thickness and resolution of SRF in the treatment of chronic CSR without any reported adverse effects. However, BCVA did not significantly improve with pharmacotherapy.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.020 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".