Outcomes of Repeat Endothelial Keratoplasty in Patients With Failed Descemet Stripping Endothelial Keratoplasty
Bibliographic record
Abstract
PURPOSE: To report the outcomes of repeat endothelial keratoplasty (EK) in patients with failed Descemet stripping endothelial keratoplasty (DSEK). METHODS: The clinical records of patients with failed DSEK who underwent repeat EK surgery at a single institution were reviewed. RESULTS: A total of 20 eyes of 20 patients (8 men and 12 women) were included. The mean age at initial DSEK surgery was 69.9 ± 11.9 years (range, 41-83 years). The causes of DSEK failure included progressive endothelial failure (8 eyes; 40%), primary graft failure (8 eyes; 40%), and endothelial rejection (4 eyes; 20%). The mean duration from primary DSEK to repeat EK was 13.1 ± 10.3 months (range, 2-33 months). Removal of the failed DSEK donor disc was performed in all eyes. Mean preoperative corrected distance visual acuity (logMAR) before repeat EK surgery was 1.76, and this improved to 0.5 (P < 0.001) at the final follow-up at 27 months. Three eyes had limited corrected distance visual acuity secondary to ocular comorbidities (age-related macular degeneration and advanced glaucomatous optic neuropathy). CONCLUSIONS: Repeat EK in patients with DSEK failure is an effective treatment modality. This is the preferred management option compared with penetrating keratoplasty because the advantages of EK surgery are maintained with repeat EK surgery.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".