Current Practice Patterns for Endothelial Keratoplasty: A Survey of Corneal Surgeons
Bibliographic record
Abstract
PURPOSE: To investigate current practice patterns among corneal specialists performing Descemet stripping automated endothelial keratoplasty (DSAEK) and Descemet membrane endothelial keratoplasty (DMEK). METHODS: An online questionnaire was distributed via the Canadian Ophthalmological Society and Kera-net (Cornea Society listserv) for the period from March to May 2025. The survey collected data on demographics, clinical practice, and intra/postoperative management strategies related to DSAEK and DMEK, including use of perioperative assistive techniques, tamponade strategies, postoperative positioning, and follow-up protocols. RESULTS: There were 70 respondents. All performed DSAEK while 82.9% performed DMEK. Most had over 5-year experience with DSAEK (85.7%) and DMEK (77.6%). Donor tissue marking was common (82.5% for DMEK; 72.9% for DSAEK), as were peripheral iridotomy/iridectomy (70.2% for DMEK; 37.1% for DSAEK), and intra/postoperative dilation (45.6% for DMEK; 61.4% for DSAEK). Intraoperative anterior segment optical coherence tomography was used by 15.8% (DMEK) and 12.9% (DSAEK) of respondents. Sulfur hexafluoride (SF6) gas is the preferred tamponade agent for routine DMEK (55.2%) but less so for DSAEK (7.1%). Intraoperative tamponade was more frequent in DSAEK (90.0%) than DMEK (67.2%). Full/near-full air/gas fills were left in 42.9% of routine DSAEK and 50.0% of DMEK. Same-day postoperative review was performed by 74.1% (DMEK) and 67.2% (DSAEK). Many surgeons discharged routine cases from corneal services by postoperative year 1 (53.4% for DMEK; 51.4% for DSAEK). CONCLUSIONS: This study sheds insights into the current endothelial keratoplasty practices among corneal specialists. Further research is needed to examine how these technique variations correlate with clinical outcomes.
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How this classification was reachedexpand
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".