OphthoEvidence report: Baseline structural optical coherence tomography biomarkers predictive of visual acuity following epiretinal membrane surgery–A systematic review and meta-analysis protocol
Bibliographic record
Abstract
BACKGROUND: Epiretinal membrane is a common retinal condition, particularly in the elderly, than can lead to reduced visual acuity and visual distortion. Structural optical coherence tomography biomarkers have shown potential for predicting visual outcomes following surgery. OBJECTIVE: To evaluate the prognostic value of baseline structural optical coherence tomography biomarkers for visual outcomes following epiretinal membrane surgery. We will assess associations between individual biomarkers and postoperative visual acuity or changes in visual acuity at 6, 12, and 24 months after surgery. METHODS: We will systematically search MEDLINE, EMBASE, CENTRAL and Web of Science from inception. Eligible studies will include randomized trials and observational studies reporting the association between any baseline optical coherence tomography biomarkers and postoperative visual acuity in patients undergoing epiretinal membrane surgery. Two reviewers will independently and in duplicate perform screening, data extraction, and risk of bias assessment. Meta-analyses using the restricted maximum likelihood random-effects model will be conducted whenever possible. The certainty of evidence for each estimate will be assessed using the GRADE approach. EXPECTED OUTCOMES: This review will analyze time-specific association between baseline structural optical coherence tomography biomarkers and postoperative visual acuity and change in visual acuity from baseline at 6, 12, and 24 months after surgery, quality of life measured using any validated scale, and adverse events.
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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 | 0.027 | 0.049 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.017 | 0.018 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.045 | 0.003 |
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".