Non-Descemet Stripping Automated Endothelial Keratoplasty (nDSAEK) for Late Endothelial Failure After Mushroom Keratoplasty: A Retrospective Analysis of Visual and Anatomical Outcomes
Bibliographic record
Abstract
Background: Mushroom penetrating keratoplasty (MPK) is an alternative to traditional penetrating keratoplasty (PK) that offers improved graft survival and reduced immunological rejection. However, MPK grafts may still experience endothelial failure over time. This study evaluates the outcomes of non-Descemet Stripping Automated Endothelial Keratoplasty (nDSAEK) as a surgical approach for endothelial decompensation following MPK. Methods: A monocentric, retrospective study was conducted at the Ophthalmology Department of Sant’Orsola-Malpighi Hospital, including patients who underwent nDSAEK for endothelial failure after MPK between 2022 and 2024. Pre- and postoperative best-corrected visual acuity (BCVA), central corneal thickness (CCT), and endothelial cell density (ECD) were assessed. Results: Eighteen eyes from 18 patients (mean age: 39.94 years) were included. Primary MPK indications were post-keratitis leucoma (77.7%), traumatic scarring (16.7%), and keratoconus (5.6%). At one year, mean BCVA improved significantly from 1.40 ± 0.42 logMAR to 0.46 ± 0.19 logMAR (p < 0.05), and mean CCT decreased from 721 ± 70.12 µm to 616 ± 52.80 µm (p < 0.05). The mean postoperative ECD was 1748 ± 100 cells/mm2, with lower eye values requiring re-bubbling. No immunological rejection or graft failures were reported. Conclusions: nDSAEK is a promising treatment for MPK endothelial failure, demonstrating good visual and anatomical outcomes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".