Cancer incidence and survival among Métis adults in Canada: results from the Canadian census follow-up cohort (1992–2009)
Bibliographic record
Abstract
BACKGROUND: Métis people are 1 of 3 Aboriginal groups recognized by the Canadian constitution. We estimated site-specific incidence rates and survival for the most common cancers among Métis adults in Canada and compared these with rates among non-Aboriginal adults in Canada. METHODS: We examined responses to the 1991 long-form census, including self-reported Métis ancestry linked to national mortality and cancer databases for followup from 1992 to 2009. We estimated age-standardized incidence rates and 5-year relative survival. We determined relative risk (RR) of cancer among Métis and non-Aboriginal adults using Poisson regression, and estimated excess mortality rate ratios using ethnicity-specific life tables. RESULTS: For all cancers and both sexes combined, cancer incidence was similar for Métis and non-Aboriginal adults. However, incidence was significantly higher among Métis adults than among non-Aboriginal adults for the following cancers: female breast (RR 1.18, 95% confidence interval [CI] 1.02-1.37), lung (RR 1.34, 95% CI 1.18-1.52), liver (RR 2.09, 95% CI 1.30-3.38), larynx (RR 1.60, 95% CI 1.03-2.48), gallbladder (RR 2.35, 95% CI 1.12-4.96) and cervix (RR 1.84, 95% CI 1.23-2.76). Métis people had poorer survival for prostate cancer (excess mortality rate ratio 2.60, 95% CI 1.52-4.46). INTERPRETATION: We found higher incidence for several cancers and poorer survival after prostate cancer among Métis adults. Several of these disparities may be related to lifestyle factors (including tobacco use, obesity and lack of cancer screening), providing evidence to support development of public health policy and health care to address cancer burden in the Métis people of 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".