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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".