Bibliographic record
Abstract
PURPOSE: To present a new technique, macular hole hydrodissection, that increases the likelihood of closure for challenging macular holes (MHs) with multiple risk factors. METHODS: A retrospective review of all consecutive eyes with idiopathic Stage 3 and 4 MHs that were either persistent (failed previous vitrectomy surgery), chronic (symptoms of central vision loss of ≥2 years or a clinical diagnosis for ≥1 year), and/or large (aperture diameter of ≥400 μm), having undergone the macular hole hydrodissection surgical technique between January 1, 2014, and May 1, 2017, from an institutional practice setting was conducted. This technique lyses retina-retinal pigment epithelium adhesions by injecting fluid into the MH and allows for successful closure as the mobile edges are then brought closer together. RESULTS: Thirty-nine eyes of 39 patients with mean MH aperture and base diameters of 549.1 ± 159.47 μm and 941.97 ± 344.14 were included. Complete anatomical closure was achieved in 87.2% (34/39) of MHs. Vision improvement was observed in 94.9% (37/39) and gain of ≥2 lines was achieved in 79.5% (31/39). Of the MHs that achieved anatomical success, 100% (34/34) had a Type 1 closure. The mean postoperative follow-up was 320.33 ± 269.04 days. CONCLUSION: The macular hole hydrodissection surgical technique improves anatomical and functional outcomes of persistent, chronic, and/or large MHs.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".