Pancreatic Cancer in Canada: Incidence and Mortality Trends from 1992 to 2005
Bibliographic record
Abstract
BACKGROUND: Pancreatic cancer is the fourth-ranking cause of death among all major malignancies in Canada and has the lowest five-year survival rate. AIM: To examine incidence and mortality trends of pancreatic cancer in Canada from 1992 to 2005, with particular emphasis on the role of cigarette smoking. METHODS: Data from Health Canada and Statistics Canada were analyzed for age-adjusted incidence and mortality trends from 1992 to 2005. The future burden of pancreatic cancer in Canada was based on population projections. RESULTS: The incidence rate of pancreatic cancer for women between 1992 and 2005 remained stable (8.49 and 8.48 cases per 100,000, respectively), and there was a decrease in the incidence for men from 11.1 per 100,000 in 1992 to 9.89 per 100,000 in 2005. This reduction may be the result of a decrease in smoking rates among Canadian men. The mortality rate of this cancer remains high. Approximately 99% of all pancreatic cancer cases occur in individuals older than 50 years of age.The total number of annual cases of pancreatic cancer in Canada is expected to more than double from 2636 cases in 2006 to 5619 in the year 2031. CONCLUSION: The incidence of pancreatic cancer in Canada from 1992 to 2005 remained relatively stable, although the incidence decreased somewhat in men, perhaps as a result of a change in smoking behaviour. The total number of cases of pancreatic cancer is expected to more than double by 2031.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| 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".