INVERTED INTERNAL LIMITING MEMBRANE FLAP TECHNIQUE WITHOUT POSTOPERATIVE FACE-DOWN POSITIONING FOR MACULAR HOLE REPAIR
Bibliographic record
Abstract
PURPOSE: To describe the outcomes of the inverted internal limiting membrane flap technique without postoperative face-down positioning for macular hole (MH) closure. METHODS: This retrospective longitudinal study identified patients who had undergone surgical repair for large (>400 µm), idiopathic MHs and did not maintain face-down positioning postoperatively. Outcome measures included anatomical success, defined as confirmation of hole closure by the optical coherence tomography scan and functional success and defined as improved best-corrected visual acuity from baseline at the last follow-up. RESULTS: Of the 63 eyes enrolled in the study, 94% patients (59 of 63) achieved anatomical success and 91% patients (57 of 63) achieved functional success. Fifteen (15) of these patients presented with a MH >600 µm. This subgroup achieved an anatomical success rate of 93% and a functional success rate of 87%. Statistically significant improvement in best-corrected visual acuity was demonstrated for all subgroups of MH size (P < 0.001). CONCLUSION: We report a high success rate of large, idiopathic MH closure with the inverted internal limiting membrane flap technique without postoperative face-down positioning. The results described in this study are favorable. However, larger studies with prospective design are warranted to explore this further.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".