Time Trends in Colorectal Cancer Incidence From 1992 to 2016 and Colorectal Cancer Mortality From 1980 to 2018 by Age Group and Geography in Canada
Bibliographic record
Abstract
INTRODUCTION: Several reports have highlighted increasing colorectal cancer (CRC) incidence among younger individuals. However, little is known about variations in CRC incidence or mortality across age subgroups in different geographical locations. We aimed to examine time trends in CRC incidence and mortality in Canada by age group and geography in this population-based, retrospective cohort study. METHODS: Individuals diagnosed with CRC from 1992 to 2016 or who died of CRC from 1980 to 2018 in Canada were studied. Geography was determined using an individual's postal code at diagnosis from the Canadian Cancer Registry or province or territory of death from the Canadian Vital Statistics Death Database. Geography was categorized into Atlantic, Central, Prairies, West, and Territories. Canadian Cancer Registry data were used to determine CRC incidence from 1992 to 2016. Canadian Vital Statistics Death data were used to determine CRC mortality from 1980 to 2018. RESULTS: Among all age groups, CRC incidence was highest in Atlantic Canada, was lowest in Western Canada, and increased with age. CRC incidence increased over time for individuals aged 20-44 years and was stable or decreased for other age groups in all regions. CRC mortality was highest in Atlantic Canada and lowest in the Prairies and Western Canada. CRC mortality decreased for individuals in all age groups and regions except among individuals aged 20-49 years in the Territories. DISCUSSION: Most of Canada has not yet seen an increase in CRC burden in the age group of 45-49 years, which is a reason to not lower the start age for CRC screening in Canada. Targeted CRC screening should be considered for individuals younger than 50 years who live in the Territories.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".