Bibliographic record
Abstract
BACKGROUND: The rising numbers of cancer diagnoses, together with improvements in survival, have led to increases in the prevalence of cancer in Canada. This article provides more precise and detailed estimates of cancer prevalence than have been available previously. DATA AND METHODS: Based on incidence data from the Canadian Cancer Registry linked with mortality data from the Canadian Vital Statistics Death Database, direct estimates of cancer prevalence as of January 1, 2005 were calculated for an extensive list of cancers, by time since diagnosis, age and sex. RESULTS: Two-, five- and ten-year cancer prevalence counts were 217,089 (675 per 100,000), 454,149 (1,412 per 100,000) and 722,833 (2,248 per 100,000), respectively. Breast (20.6% of ten-year prevalent cases), prostate (18.7%) and colorectal cancer (12.9%) were the most prevalent, together accounting for just over half of all cases. Prevalence proportions for all cancers combined increased dramatically with age, peaking at ages 80 to 84; proportions were higher in females than in males before age 60, and higher in males thereafter. INTERPRETATION: Prevalence data tabulated according to type of cancer, age and time since diagnoses provide important information about the demand for cancer-related health care and social services.
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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 | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".