Early-Onset Colorectal Cancer Incidence, Staging, and Mortality in Canada: Implications for Population-Based Screening
Bibliographic record
Abstract
INTRODUCTION: The incidence of early-onset colorectal cancer (eoCRC) has been increasing in North America. Debate remains as to whether the trends by topography, histology, stage, or mortality in this population are amenable to intervention from screening. METHODS: CRC incidence (2000-2017) and mortality (2000-2018) data were obtained from the Canadian Cancer Registry and Vital Statistics. Annual percentage changes (APC) in the incidence (topography and histology) and mortality of eoCRC were estimated using joinpoint regression. Incidence of late-stage CRC (III or IV) versus early-stage CRC (I or II) was compared between the eoCRC (age 20-49 years) and eligible screening (age 50-74 years) groups with Poisson regression. RESULTS: Among women aged 20-49 years, the incidence of CRC significantly increased from 2000 to 2017 in both the distal colon (APC = 1.40) and rectum (APC = 3.00), whereas for men aged 20-49 years, the CRC incidence increased in the proximal colon (APC = 1.10), distal colon (APC = 3.00), and rectum (APC = 3.70). Among both men and women aged 20-49 years, the incidence of nonmucinous adenocarcinomas significantly increased (APC: 1.90 and 2.30, respectively), whereas mucinous adenocarcinomas decreased for women (APC = -1.60) and remained stable for men. Adults aged 30 to 49 years, when diagnosed with CRC, had a significantly higher risk of being diagnosed with a late-stage CRC compared with those in the age group of 50-74 years. Rectal cancer mortality increased from 2000 to 2018 in the eoCRC group (APC for women and men 3.80 and 3.40, respectively). DISCUSSION: Emerging data support future modifications to guidelines on screening for eoCRC in Canada. Further research is required on the effect, cost-effectiveness, and risk prediction for targeted screening within this group.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".