Descemet Membrane Endothelial Keratoplasty After Penetrating Keratoplasty: Features for Success
Bibliographic record
Abstract
PURPOSE: To evaluate Descemet membrane endothelial keratoplasty (DMEK) in the setting of failed penetrating keratoplasty (PKP) and to identify factors associated with DMEK success and failure after PKP. METHODS: A retrospective chart review of patients who underwent DMEK for failed PKP at Toronto Western Hospital, Canada, between 2014 and 2017 was performed. Demographic characteristics, number of previous transplants, intraoperative and postoperative complications, best spectacle-corrected visual acuity (BSCVA), and endothelial cell density were analyzed. RESULTS: Twenty-eight eyes were included in the study. Rebubbling intervention was performed in 12 eyes (43%) within the first postoperative weeks. Five eyes (18%) developed graft rejection episodes. Twelve eyes (43%) had to be regrafted after DMEK surgery and were deemed failures (because of persistent Descemet membrane detachment, rejection episode that led to secondary failure, and infection). BSCVA before DMEK was significantly worse in the eyes that failed than those that did not [1.97 ± 0.85 and 1.2 ± 0.56 logMAR, respectively, (P = 0.01)]. Rebubbling was required in 75% of eyes in the failure group compared with 19% in the success group (P = 0.002). Six of the 16 eyes (37.5%) in the success group underwent femtosecond laser-enabled DMEK, whereas this technique was not used in any of the eyes in the failure group (P = 0.017). CONCLUSIONS: DMEK is a viable option for cases of failed PKP. DMEK failure after PKP might be associated with lower visual acuity before DMEK surgery, higher number of rebubble interventions, and manual descemetorhexis rather than femtosecond laser-enabled DMEK.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".