Comparison of Descemet Stripping Automated Endothelial Keratoplasty and Descemet Membrane Endothelial Keratoplasty in the Treatment of Failed Penetrating Keratoplasty
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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