Comparing interventions for chronic central serous chorioretinopathy: A network meta-analysis
Bibliographic record
Abstract
We compare efficacy of treatments for chronic central serous chorioretinopathy (CSCR) > 3 months. Four treatment classes were considered: photodynamic therapy (PDT), subthreshold laser therapies (SLT), mineralocorticoid receptor antagonists (MRA) and antivascular endothelial growth factor (anti-VEGF) agents. Pairwise and network meta-analyses (NMA) of the primary outcomes (complete resolution of subretinal fluid (SRF), mean change in best corrected visual acuity (BCVA as logMAR) and mean change in SRF) and secondary outcomes (mean change in central retinal thickness, and central choroidal thickness (μm), recurrence of SRF, and adverse events) at 3, 6, and 12 months were compared. Confidence in Network Meta-Analysis (CINeMA) informed the certainty of NMA evidence. Eleven RCTs of 458 eyes (450 patients) were included. NMA at 3 months showed that both PDT and SLT were superior to control for resolution of SRF (OR 4.83; 95% CI 1.72-13.55 and 2.27; 1.14-4.49, respectively) and SLT was superior to control for improving BCVA (MD -0.10; -0.17 to -0.04). PDT was superior to SLT for improving CRT (MD -42.88; -75.27 to -10.50). On probability ranking, PDT and SLT were consistently the best-ranked treatments for each outcome at 3 months, but low confidence of evidence and paucity of studies preclude definitive conclusions.
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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 | 0.015 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.038 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".