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 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".