Cancer incidence, stage at diagnosis and outcomes among Manitoba First Nations people living on and off reserve: a retrospective population-based analysis
Bibliographic record
Abstract
Background: Substantial cancer-related disparities exist between First Nations and non-Indigenous Canadians. The objectives of this study were to compare cancer incidence, stage at diagnosis and mortality outcomes between Status First Nations people living on reserve and off reserve in Manitoba. Methods: We conducted a retrospective analysis of population-level administrative health databases in Manitoba. Cancers diagnosed between Apr. 1, 2004, and Mar. 31, 2011, were linked with the Indian Registry System and 5 provincial databases. We compared differences in baseline characteristics, cancer incidence, site and stage at diagnosis between Status First Nations patients living on and off reserve. Linear regression models examined trends in annual cancer incidence. Cox proportional hazard regression models examined mortality. Results: There were 1524 newly diagnosed cancers among Status First Nations people in Manitoba between Apr. 1, 2004, and Mar. 31, 2011. First Nations people living on reserve were significantly older than those living off reserve (p < 0.001) and had higher Charlson Comorbidity Index scores at diagnosis (p = 0.01). A lower proportion of on-reserve patients than off-reserve patients were diagnosed with stage I cancers (21.7% v. 26.9%, p = 0.02). There were no differences in annual cancer incidence between groups. The adjusted incidence of cancer over the combined study years was higher in the off-reserve group than in the on-reserve group (287.9 v. 247.9 per 100 000, p = 0.02). No significant differences in mortality were found. Interpretation: The lower proportion of on-reserve patients diagnosed with cancer at stage I is concerning, as it suggests less access to screening services or delays in diagnosis. Further research is needed to understand patterns in diagnosis and differences in cancer site and overall cancer incidence between First Nations people living on and off reserve.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".