Anatomic and Functional Outcomes of Lamellar Macular Hole and Epiretinal Membrane Foveoschisis Surgery: Predictive Factors and Associated Complications — A Retrospective Interventional Study
Bibliographic record
Abstract
Purpose: To analyze the anatomic and functional outcomes of lamellar macular hole (LMH) and epiretinal membrane foveoschisis (ERMF) surgery. Patients and Methods: This is a retrospective interventional cohort study of ninety patients with unilateral idiopathic LMH or ERMF who underwent pars plana vitrectomy (PPV) with membrane peeling between 2014 and 2021. We evaluated the anatomic and functional success of PPV with membrane peeling for treating LMH and ERMF, compared surgical outcomes between the two entities, and identified predictive factors for anatomical and functional success. Primary outcomes included final postoperative best-corrected visual acuity (BCVA) and LMH or ERMF closure. Variables associated with final BCVA were assessed using a multiple linear regression model. Results: 51 subjects presented with ERMF, while 39 presented with LMH. LMH or ERMF closure occurred in 80 cases. LMH cases had a lower rate of closure (LMH closure rate: 76.9%, vs ERMF closure rate: 98.0%, p=0.002) and were more at risk of developing a postoperative macular hole (p=0.008). A significant difference was observed between median [Q1, Q3] preoperative BCVA (0.42 [0.26, 0.61] logMAR) and final BCVA (0.31 [0.14, 0.48] logMAR, p=0.024). BCVA varied from 0.52 [0.40, 0.74] logMAR preoperatively to 0.36 [0.30, 0.66] logMAR postoperatively in the LMH subgroup (p=0.060), and from 0.32 [0.20, 0.54] logMAR preoperatively to 0.22 [0.10, 0.40] logMAR postoperatively in the ERMF subgroup (p=0.146). LMH without epiretinal proliferation (β=0.194, p=0.040) was associated with worse final BCVA in multivariate analysis. Conclusion: Results support the effectiveness of PPV as a treatment for LMH. LMHs had worse anatomic outcomes than ERMFs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".