Comparing cancer incidence, stage at diagnosis and outcomes of First Nations and all other Manitobans: a retrospective analysis
Bibliographic record
Abstract
BACKGROUND: Globally, epidemiological evidence suggests cancer incidence and outcomes among Indigenous peoples are a growing concern. Although historically cancer among First Nations (FN) peoples in Canada was relatively unknown, recent epidemiological evidence reveals a widening of cancer related disparities. However evidence at the population level is limited. The aim of this study was to explore cancer incidence, stage at diagnosis, and outcomes among status FN peoples in comparison with all other Manitobans (AOM). METHODS: All cancers diagnosed between April 1, 2004 and March 31, 2011 were linked with the Indian Registry System and five provincial healthcare databases to compare differences in characteristics, cancer incidence, and stage at diagnosis and mortality of the FN and AOM cohorts. Cox proportional hazard regression models were used to examine mortality. RESULTS: The FN cohort was significantly younger, with higher comorbidities than AOM. A higher proportion of FN people were diagnosed with cancer at stages III (18.7% vs. 15.4%) and IV (22.4% vs. 19.9%). Cancer incidence was significantly lower in the FN cohort, however, there were no significant differences between the two cohorts after adjusting for age, sex, income and area of residence. No significant trends in cancer incidence were identified in either cohort over time. Mortality was generally higher in the FN cohort. CONCLUSIONS: Despite similar cancer incidence, FN peoples in Manitoba experience poorer survival. The underlying causes of these disparities are not yet understood, particularly in relation to the impact of colonization and other determinants of health.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| 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".