New trends in corneal transplants at the University of Toronto
Bibliographic record
Abstract
OBJECTIVE: To assess trends in surgical procedures and indications for all corneal transplants performed at the University of Toronto. DESIGN: Retrospective cross-sectional study. PARTICIPANTS: One thousand one hundred and four consecutive corneal transplants performed at the Kensington Eye Institute (KEI). METHODS: Demographic, clinical, and pathological data retrieved from the Ophthalmic Pathology Laboratory on all corneal transplants performed at the KEI from January 2014 to December 2016. RESULTS: Over 3 years, partial-thickness lamellar keratoplasties were performed in 880 cases (80%) while full-thickness penetrating keratoplasties (PKP) accounted for 224 cases (20%). Leading causes of corneal transplant were Fuchs' dystrophy (42%), graft failure (17%), bullous keratopathy (15%), and keratoconus (15%). Graft failure (40%) and keratoconus (31%) were the leading causes for PKP. Descemet's membrane endothelial keratoplasty (DMEK) accounted for 37% of cases, Descemet's stripping automated endothelial keratoplasty (DSAEK) for 30%, and deep anterior lamellar keratoplasty (DALK) for 13%. By 2016, partial-thickness procedures had increased by 10%, accounting for 85% of all procedures. In addition, DMEK increased by 26%, DSAEK decreased by 13%, and PKP decreased by 11%. Fuchs' dystrophy remained the leading indication for DMEK (67%) and DSAEK (42%) procedures. In 2016, 73% of DALK procedures were for the treatment of keratoconus. CONCLUSIONS: Partial-thickness corneal transplants now account for 85% of all current graft procedures, and DMEK has emerged as the procedure of choice. Graft failure continues to be the leading indication for full-thickness grafts. Longitudinal studies are needed to determine whether these new trends persist and their future impact on graft failures.
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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 | 0.000 | 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) | 0.025 | 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".