Comparison of Descemet Stripping Automated Endothelial Keratoplasty and Descemet Membrane Endothelial Keratoplasty in the Treatment of Failed Penetrating Keratoplasty
Bibliographic record
Abstract
PURPOSE: To compare the outcomes of Descemet stripping automated endothelial keratoplasty (DSAEK) with Descemet membrane endothelial keratoplasty (DMEK) for the treatment of failed penetrating keratoplasty (PKP). METHODS: This is a retrospective chart review of patients with failed PKP who underwent DMEK or DSAEK. The median follow-up time for both groups was 28 months (range 6-116 months). Data collection included demographic characteristics, number of previous corneal transplants, previous glaucoma surgeries, best-corrected visual acuity, endothelial cell density, graft detachment and rebubble rate, rejection episodes, and graft failure. RESULTS: Twenty-eight eyes in the DMEK group and 24 eyes in the DSAEK group were included in the analysis. Forty-three percent of eyes in the DMEK group and 50% of eyes in the DSAEK group had to be regrafted because of failure (P = 0.80). The most common reason for failure was persistent graft detachment (58%) in the DMEK group and secondary failure (58%) in the DSAEK group; hence, the time between endothelial keratoplasty and graft failure differed significantly between the groups (P = 0.02). Six eyes (21%) in the DMEK group and 7 eyes (29%) in the DSAEK group developed graft rejection (P = 0.39). Rejection was the cause of failure in 67% and 71% in the DMEK and DSAEK groups, respectively. The best-corrected visual acuity 6 months after surgery was better in the DMEK group compared with the DSAEK group (P = 0.051). CONCLUSIONS: Both DSAEK and DMEK have a role in treating PKP failure. Primary failure due to persistent graft detachment was significantly higher in the DMEK group, although the overall failure rate in the medium term was similar.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".