Surgeon Preferences for Endothelial Keratoplasty in Canada
Bibliographic record
Abstract
PURPOSE: To quantify practice patterns and assess attitudes and barriers to performing Descemet membrane endothelial keratoplasty (DMEK) in Canada. METHODS: An anonymous online survey was distributed to all corneal surgeons included in the Canadian Ophthalmological Society's database. RESULTS: Of 70 listed surgeons, 41 responses were collected (58.6% response rate). Most respondents were practicing in university hospitals (43.9%) or private practice (43.9%) and were involved in residency teaching (77.5%). Most respondents performed DMEK surgery (78%), and most surgeons prepared their own DMEK grafts (62%). Surgeons who were in practice for more than 25 years were less likely to perform DMEK (75% vs. 13%, P = 0.009) and performed fewer corneal transplantation in the previous year (mean 28 vs. 44, P = 0.022). Those who were not performing DMEK reported access to preprepared tissue (77.8%), access to wet laboratory courses (50%), and assistance or mentorship (50%) as common facilitators to start performing DMEK surgery. CONCLUSIONS: DMEK is the preferred surgery for endothelial disease among Canadian corneal surgeons. Eye banks play a key role in increased adoption by ensuring an adequate supply of tissue and prestripping tissue for surgeons new to DMEK to be confident in performing it. Ensuring adequate supply of donor tissue and supplementary surgeon training can ensure that DMEK surgery is widely available in Canada.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".