Long-term trends in the incidence and relative survival of colorectal cancer in Canada: A population-based study
Bibliographic record
Abstract
Background and aim: Mortality rates for colorectal cancer have decreased since the mid 1990s. This article provides anup-to-date report on the trends in incidence and survival of colorectal cancer in Canada. In this study we investigate the long-termtrends in the incidence and relative survival ratio of colorectal cancer in Canada over the period of 1992-2008. Patients and methods: Patients with primary colorectal cancer were selected from the Canadian Cancer Registry (CCR) dataset.Patients younger than 18 years of age were excluded. A flexible parametric model was used to estimate two- and five-year relativesurvival ratios and excess mortality rate. Results: In total 159,360 patients with invasive colorectal cancer were identified of which 84,856 (53.2%) were male, 96,495(60.6%) were diagnosed with colon cancer, and 62,865 (39.4%) with the cancer of rectum. Mean age at diagnosis was 68.2 years( SD = 12.1) for men and 70.9 years ( SD = 13.0) for women. The incidence of colorectal cancer remained almost the same formen and women in this period. Except for patients with 70 years and older, two- and five-year relative survival ratios slightlyimproved over time for both sexes. Conclusion: The incidence rate of colorectal cancer remained unchanged and the two- and five-year relative survival ratiossteadily increased for men and women over the study period. Although we used data up to 2008, screening programs in Canadahave been implemented since 2010, therefore, incidence rates may change thereafter and advancements in treatment could furtherimprove the survival of colorectal cancer patients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| 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) | 0.001 | 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".