Current indications and surgical approaches to corneal transplants at the University of Toronto: A clinical-pathological study
Bibliographic record
Abstract
OBJECTIVE: To determine the most common reasons and surgical approaches for corneal graft surgery at the Kensington Eye Institute (KEI), University of Toronto. DESIGN: Retrospective cross-sectional study. PARTICIPANTS: A total of 229 consecutive corneal transplants performed at the KEI. METHODS: Demographic, clinical, and pathological data on all 2012 and 2013 corneal transplants were collected. RESULTS: The mean age for corneal transplants was 65 ± 16 years; 39% were full-thickness penetrating keratoplasties (PK) and 61% were partial-thickness. Graft failure (30%), infection (18%), and keratoconus (17%) were the leading indications for PK. Fuchs' dystrophy (40%) and bullous keratopathy (24%) were main causes for partial-thickness procedures. Among partial-thickness approaches, Descemet's stripping automated endothelial keratoplasty (DSAEK), deep anterior lamellar keratoplasty (DALK), and Descemet's membrane endothelial keratoplasty (DMEK) procedures accounted for 68%, 16%, and 16%, respectively. Fuchs' dystrophy (40%) and bullous keratopathy (33%) were the most common indications for DSAEK. Keratoconus (57%) and corneal scarring (35%) were the most common indications for DALK, whereas Fuchs' dystrophy (82%) accounted for most DMEK procedures. The most common reasons for all corneal grafts were Fuchs' dystrophy (25%), bullous keratopathy (21%), graft failure (17%), and keratoconus (12%). CONCLUSIONS: Almost two-thirds of all corneal transplant procedures at the University of Toronto are partial thickness procedures. A failed graft was found to be the most common indication for full-thickness transplants. Fuchs' dystrophy was the most common indication for a partial-thickness approach, most often treated by DSAEK. Longitudinal data are needed to determine whether partial-thickness surgeries will improve graft survival and reduce the need for regraft.
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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 | 0.000 |
| 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) | 0.001 | 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".