Cancer mortality in Yukon 1999–2013: elevated mortality rates and a unique cancer profile
Bibliographic record
Abstract
Background: Although cancer is the leading cause of death in Canada, cancer in the North has been incompletely described.Objective: To determine cancer mortality rates in the Yukon Territory, compare them with Canadian rates, and identify major causes of cancer mortality.Design: The Yukon Vital Statistics Registry provided all cancer deaths for Yukon residents between 1999-2013. Age-standardised mortality rates (ASMRs) were calculated using direct standardisation and compared with Canadian rates. Standardised mortality ratios (SMRs) were calculated using indirect standardisation relative to age-specific rates from Canada, British Columbia (BC), and three sub-provincial BC administrative health regions : Interior Health (IH), Northern Health (NH) and Vancouver Coastal Health (VCH). Trends in smoothed ASMRs were examined with graphical methods.Results: Yukon’s all-cancer ASMRs were elevated compared with national and provincial rates for the entire period. Disparities were greatest compared with the urban VCH: prostate (SMRVCH=246.3, 95% CI 140.9–351.6), female lung (SMRVCH=221.2, 95% CI 154.3–288.1), female breast (SMRVCH=169.0 95% CI, 101.4–236.7), and total colorectal (SMRVCH=149.3, 95% CI 101.8–196.8) cancers were significantly elevated. Total stomach cancer mortality was significantly elevated compared with all comparators.Conclusions: Yukon cancer mortality rates were elevated compared with national, provincial, urban, and southern-rural jurisdictions. More research is required to elucidate these differences.
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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 | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".