Anatomic and Functional Outcomes of Lamellar Macular Hole Surgery: Predictive Factors and Associated Complications
Bibliographic record
Abstract
ABSTRACT Purpose To analyze the anatomic and functional outcomes of lamellar macular hole (LMH) surgery. Patients and methods This is a retrospective interventional cohort study of ninety patients with unilateral idiopathic LMH who underwent pars plana vitrectomy (PPV) with membrane peeling for LMH between 2014 and 2021. We evaluated the anatomic and functional success of PPV with membrane peeling for treating LMH, compared surgical outcomes between the two LMH subtypes (“true” LMH and epiretinal foveoschisis (ERMF)), and identified predictive factors for anatomical and functional success. Primary outcomes included final postoperative best-corrected visual acuity (BCVA) and LMH closure. Variables associated with final BCVA were assessed using a multiple linear regression model. Results 51 subjects presented with ERMF, while 39 presented with “true” LMH. LMH closure occurred in 80 cases. “True” LMH cases had a lower rate of closure (“true” LMH closure rate: 76.9%, vs. ERMF closure rate: 94.2%, p=0.005) 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]) and final BCVA (0.31 [0.14, 0.48], p=0.024). “True” 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. “True” 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 | 0.003 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".