Visual Outcomes and Complications of Epiretinal Membrane Removal Secondary to Rhegmatogenous Retinal Detachment
Bibliographic record
Abstract
Purpose To examine visual outcomes after surgery for removal of epiretinal membrane after retinal detachment (RD) repair, and to identify possible complications. Design Retrospective case series. Participants Seventy-five patients who underwent surgery for removal of an epiretinal membrane after RD repair. Methods Retrospective chart review of 75 patients who underwent surgery for removal of an epiretinal membrane after RD repair over a 10-year period. Follow-up ranged from 6 to 124 months (mean, 49). Main Outcome Measures Measurement of final visual acuity (VA). Results After epiretinal membrane removal, VA increased 0.43 logarithms of the minimum angle of resolution (95% confidence interval, 0.27–0.58; t = 5.47, P = 0.000). Sixty-six patients (88%) had a VA at final follow-up greater than their preoperative VA. Forty-nine patients (70%) had a final VA equal to or greater than their best VA after RD repair. Final VA of 20/60 or better was achieved in 65.3% of eyes. Of the 43 eyes that were phakic at the beginning of the study, 34 (79.1%) underwent cataract extraction before final follow-up. Five eyes (6.7%) had a recurrent RD after membrane removal. Sixteen eyes (21.3%) developed a recurrent epiretinal membrane, 1 of which (1.3% of all eyes) was visually significant, requiring repeat surgery. Conclusions Epiretinal membrane development is a known complication of RD repair. Visual acuity can be improved in these patients by removal of the epiretinal membrane, with excellent results. In this series, recurrent membrane formation is not visually significant. Careful follow-up is needed to detect recurrent RD.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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 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".