Cancer Incidence, Mortality, and Stage at Diagnosis in First Nations Living in Manitoba
Bibliographic record
Abstract
BACKGROUND: In the present study, we examined breast (bca) and colorectal cancer (crc) incidence and mortality and stage at diagnosis for First Nations (fn) individuals and all other Manitobans (aoms). METHODS: Several population-based databases were linked to determine ethnicity and to calculate age-standardized incidence and mortality rates. Logistic regression was used to compare bca and crc stage at diagnosis. RESULTS: From 1984-1988 to 2004-2008, the incidence of bca increased for fn and aom women. Breast cancer mortality increased for fn women and decreased for aom women. First Nations women were significantly more likely than aom women to be diagnosed at stages iii-iv than at stage i [odds ratio (or) for women ≤50 years of age: 3.11; 95% confidence limits (cl): 1.20, 8.06; or for women 50-69 years of age: 1.72; 95% cl: 1.03, 2.88). The incidence and mortality of crc increased for fn individuals, but decreased for aoms. First Nations status was not significantly associated with crc stage at diagnosis (or for stages i-ii compared with stages iii-iv: 0.98; 95% cl: 0.68, 1.41; or for stages i-iii compared with stage iv: 0.91; 95% cl: 0.59, 1.40). CONCLUSIONS: Our results underscore the need for improved cancer screening participation and targeted initiatives that emphasis collaboration with fn communities to reduce barriers to screening and to promote healthy lifestyles.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".