Brief Report: Trends in Incidence, Mortality, and Disability-Adjusted Life Years for Early-Onset Colorectal Cancer in Canada Between 1990 and 2019
Bibliographic record
Abstract
Background: Colorectal cancer is the third most common malignancy globally. Early-onset colorectal cancer (EOCRC) is becoming a growing healthcare focus globally, particularly in North America. We estimated trends in incidence, mortality, and disability-adjusted life years (DALYs) for EOCRC in Canada between 1990 and 2019. Methods: We used the Global Burden of Diseases Study to evaluate trends in incidence, mortality, and DALYs for EOCRC in Canada between 1990 and 2019. Rates were estimated per 100,000 persons at risk with associated uncertainty intervals (UIs). Annual percentage changes (APC) were estimated using joinpoint regression with 95% confidence intervals (CIs). Results: In 2019, the incidence, mortality, and DALYs rates for EOCRC were 10.89 (95% UI 8.09, 14.34), 2.24 (95% UI 2.00, 2.51), and 111.37 (95% UI 99.34, 124.78) per 100,000 individuals, respectively. Incidence increased during the study period by 1.12%/year (95% CI 1.03%, 1.22%; p < 0.001). The largest increase in incidence in EOCRC occurred between 1990 and 2007, with an APC of 2.23% (95% CI 2.09%, 2.37%; p < 0.001). Mortality (APC 2.95%, 95% CI 1.89%, 4.02%; p < 0.001) and DALY (APC 2.96%, 95% CI 1.84%, 4.09%; p < 0.001) rates increased for males between 2001 and 2006. Conclusions: Our study reveals a substantial burden in EOCRC in Canada, with a significant increase in incidence.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".