THE CURRENT SURGICAL MANAGEMENT OF LARGE, RECURRENT, OR PERSISTENT MACULAR HOLES
Bibliographic record
Abstract
PURPOSE: To evaluate the current surgical options available for the management of large (>400 μm), recurrent, or persistent macular holes (MHs). METHODS: A review of the literature was performed, focusing on the epidemiology, pathophysiology, diagnosis, and surgical treatments of large, recurrent, or persistent MHs. Based on this review, a comprehensive overview was provided regarding the topic of large, recurrent, or persistent MHs and focused on recent surgical management updates. RESULTS: For large MHs, variations of the inverted internal limiting membrane flap technique demonstrated promising rates of primary hole closure and significant visual acuity improvements. For recurrent or recalcitrant MHs, early repeat vitrectomy with extension of the internal limiting membrane peel remains the most straightforward and optimal surgical technique to achieve secondary closure. Regardless of the surgical approach, the goal of each technique described is to induce or aid in stimulating gliosis within the MH to maximize closure. CONCLUSION: Despite the high success rate of modern MH surgery, large, recurrent, or persistent MHs remain a challenge for retinal surgeons. This review provides a detailed summary on the rationality and efficacy of current surgical options.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".