Trends in incidence, mortality, and survival for kidney cancer in Canada, 1986–2007
Bibliographic record
Abstract
PURPOSE: Kidney cancer is one of the fastest rising cancers worldwide. We aimed to examine the trends in incidence, mortality, and survival for this cancer in Canada. METHODS: Incidence data for kidney cancer for 1986-2010 were from the Canadian Cancer Registry and the National Cancer Incidence Reporting System. These data were only available up to 2007 for the province of Quebec and consequently for the same year nationally, for Canada. Mortality data for 1986-2009 were from the Canadian Vital Statistics Death Database. Changes in age-standardized rates were analyzed by Joinpoint regression. Incidence rates were projected to 2025 using a Nordpred age-period-cohort model. Five-year relative survival ratios (RSR) were analyzed for 2004-2008 and earlier periods. RESULTS: Between 1986 and 2007, the age-standardized incidence rate (ASIR) per 100,000 rose from 13.4 to 17.9 in males and 7.7 to 10.3 in females. Annual increases in ASIR were greatest for age groups <65 years (males) and ≥65 years (females). The ASIRs increased significantly over time in both sexes for renal cell carcinoma (RCC) but not for other kidney cancer types. RCC rates are projected to increase until at least 2025. Mortality rates decreased only slightly in each sex since 1986 (0.4%/year in males; 0.8%/year in females). The 5-year RSR for kidney cancer was 68% but differed largely by morphology and age, and has increased slightly over time. CONCLUSIONS: The incidence rate of kidney cancer in Canada has risen since at least 1986, led largely by RCC. Increasing detection of incidental tumors, and growing obesity and hypertension rates are possible factors associated with this increase. Greater prevention of modifiable risk factors for kidney cancer is needed.
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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 | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".