Human amniotic membrane for the treatment of large and refractory macular holes: a retrospective, multicentric, interventional study
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: The purpose of the current study is to report the anatomical and functional results of off-label human amniotic membrane graft as primary intervention to repair large to giant macular holes and in reoperations when wide internal limiting membrane peeling was unsuccessful. METHODS: Retrospective chart review was carried out in five different centers to identify all cases that had undergone off-label human amniotic membrane graft for the treatment of large or failed macular holes (MH). Data collected included age, gender, other concomitant diagnosis, symptoms duration, lens status, number of previous surgeries, macular hole measurements (minimum and base linear diameters), mean post-operative follow-up (months), and pre- and post-operative best corrected visual acuity (BCVA). Main outcome measures were anatomical MH closure rates and final BCVA (in logMAR). Nonparametric Wilcoxon rank-sum test was used because the data was not normally distributed, a P values < 0.05 were considered statistically significant. RESULTS: Nineteen eyes of 19 patients were identified and included in the study. Mean age was 66.21 ± 14.96 years and predominantly females (84%). All eyes had successfully closed MH with a single intervention with no recurrences during a mean of 9 ± 3.87 months follow-up. The median BCVA in logMAR preoperative was 1.30 ± 0.44 (0.80-2.0), approximately 20/400 on Snellen chart and the median BCVA in logMAR postoperative was 1.0 ± 0.72 (0.4-3.0) approximately 20/200 (p < 0.0001) with median of three lines of visual improvement. CONCLUSION: The use of human amniotic membrane graft seems to be a viable and effective alternative for the treatment of large and persistent macular holes. However, further larger prospective controlled studies are necessary to confirm our preliminary results of this new surgical technique.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 it