Projected estimates of cancer in Canada in 2022
Bibliographic record
Abstract
BACKGROUND: Regular cancer surveillance is crucial for understanding where progress is being made and where more must be done. We sought to provide an overview of the expected burden of cancer in Canada in 2022. METHODS: We obtained data on new cancer incidence from the National Cancer Incidence Reporting System (1984-1991) and Canadian Cancer Registry (1992-2018). Mortality data (1984-2019) were obtained from the Canadian Vital Statistics - Death Database. We projected cancer incidence and mortality counts and rates to 2022 for 22 cancer types by sex and province or territory. Rates were age standardized to the 2011 Canadian standard population. RESULTS: An estimated 233 900 new cancer cases and 85 100 cancer deaths are expected in Canada in 2022. We expect the most commonly diagnosed cancers to be lung overall (30 000), breast in females (28 600) and prostate in males (24 600). We also expect lung cancer to be the leading cause of cancer death, accounting for 24.3% of all cancer deaths, followed by colorectal (11.0%), pancreatic (6.7%) and breast cancers (6.5%). Incidence and mortality rates are generally expected to be higher in the eastern provinces of Canada than the western provinces. INTERPRETATION: Although overall cancer rates are declining, the number of cases and deaths continues to climb, owing to population growth and the aging population. The projected high burden of lung cancer indicates a need for increased tobacco control and improvements in early detection and treatment. Success in breast and colorectal cancer screening and treatment likely account for the continued decline in their burden. The limited progress in early detection and new treatments for pancreatic cancer explains why it is expected to be the third leading cause of cancer death in Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| 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) | 0.011 | 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".