Projected estimates of cancer in Canada in 2020
Bibliographic record
Abstract
BACKGROUND: report. METHODS: We obtained incidence data from the National Cancer Incidence Reporting System (1984-1991) and Canadian Cancer Registry (1992-2015). Mortality data (1984-2015) were obtained from the Canadian Vital Statistics - Death Database. All databases are maintained by Statistics Canada. Cancer incidence and mortality counts and age-standardized rates were projected to 2020 for 23 cancer types by sex and geographic region (provinces and territories) for all ages combined. RESULTS: An estimated 225 800 new cancer cases and 83 300 cancer deaths are expected in Canada in 2020. The most commonly diagnosed cancers are expected to be lung overall (29 800), breast in females (27 400) and prostate in males (23 300). Lung cancer is also expected to be the leading cause of cancer death, accounting for 25.5% of all cancer deaths, followed by colorectal (11.6%), pancreatic (6.4%) and breast (6.1%) cancers. Incidence and mortality rates will be generally higher in the eastern provinces than in the western provinces. INTERPRETATION: The number of cancer cases and deaths remains high in Canada and, owing to the growing and aging population, is expected to continue to increase. Although progress has been made in reducing deaths for most major cancers (breast, prostate and lung), there has been limited progress for pancreatic cancer, which is expected to be the third leading cause of cancer death in Canada in 2020. Additional efforts to improve uptake of existing programs, as well as to advance research, prevention, screening and treatment, are needed to address the cancer burden in Canada.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".