Did Collagen Cross-Linking Reduce the Requirement for Corneal Transplantation in Keratoconus? The Canadian Experience
Bibliographic record
Abstract
PURPOSE: To investigate the relationship between corneal collagen cross-linking (CXL) and the number of corneal transplants required for the treatment of keratoconus (KCN) in 2 major Canadian provinces. METHODS: This is a retrospective review of all corneal transplantation performed in Ontario and British Columbia over an 18-year period (1998-2016). Data were collected at the Eye Bank of Canada-Ontario/British Columbia Divisions. The primary outcome was to determine the change in proportion and absolute number of corneal transplants required for treatment of KCN since the introduction of CXL in Canada in 2008. RESULTS: A total of 31,943 grafts were included. Overall, the mean age of participants was 39.3 ± 2.2 years, with our cohort being composed of 28% of women and 72% of men. The results showed a significant decrease in the proportion of total transplants required for KCN between 1998 and 2016 [1998-2008 (pre-CXL), range: 14.77%-12.63%; 2009-2016 (post-CXL), range: 12.98%-5.50%, P < 0.001]. However, there was no change in the absolute number of grafts performed for KCN over this time (pre-CXL: 179 ± 26 grafts; post-CXL: 198 ± 27 grafts; P = 0.5), whereas the total number of grafts (pre-CXL: 1318 ± 183 grafts; post-CXL: 2181 ± 404; P < 0.001) and endothelial keratoplasties (pre-CXL: 59 ± 108; post-CXL: 966 ± 431 grafts; P < 0.001) increased significantly. In addition, there were no changes in penetrating keratoplasty/deep anterior lamellar keratoplasty (DALK) performed for indications other than KCN (pre-CXL: 1080 ± 157; post-CXL: 1017 ± 92; P > 0.5). CONCLUSIONS: Although there has been a significant decrease in the proportion of corneal graft rates for KCN since the introduction of CXL as a factor of all transplants performed for all indications, this result is most likely because of an increase in endothelial keratoplasties rather than decreased transplants performed for definitive treatment.
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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 | 0.002 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".