Uveal melanoma incidence trends in Canada: 1992–2010 vs. 2011–2017
Bibliographic record
Abstract
Background/aims: Uveal melanoma is the most common type of non-cutaneous melanoma and the most common ocular malignancy in the adult population, especially affecting Caucasians (98% of cases). Despite its low incidence rate, we have noted increasing incidence trends in recent years. Methods: We analyzed uveal melanoma incidence data using the Canadian Cancer Registry (CCR) for 2011-2017 years. The data was examined using the International Classification of Diseases for Oncology, Third Edition, codes for all uveal melanoma subtypes. The data for 2011-2017 was then compared to previously published work by our research group for uveal melanoma incidence in Canada between 1992 and 2010 using the same methodology. Results: Between 2011 and 2017, 1,215 patients were diagnosed with uveal melanoma, 49% of whom were females. The percentage distribution of uveal melanoma between the sexes was similar between 1992-2010 and 2011-2017, whereby of the 2,215 diagnoses of uveal melanoma in 1992-2010, 47.9% were females. The change in the incidence rate for this cancer has doubled between 1992-2010 and 2011-2017, from 0.074 to 0.15 cases per million individuals per year. Our study documents that the Canadian 2011-2017 age-standardized incidence rate (ASIR) for uveal melanoma against the World Health Organization (WHO) 2000-2025 world population standard was 5.09 cases per million individuals per year (95% confidence interval, 4.73-5.44), as compared with the 1992-2010 rate of 3.34 cases per million individuals per year (95% confidence interval, CI 3.20 to 3.47). Conclusion: This work demonstrates an ongoing, steady increase in uveal melanoma incidence in Canada in recent years.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".