Stage at Diagnosis and Comorbidity Influence Breast Cancer Survival in First Nations Women in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: Indigenous populations in Canada and abroad have poorer survival after a breast cancer diagnosis compared with their geographic counterparts; however, the influence of many demographic, personal, tumor, and treatment factors has not been examined to describe this disparity according to stage at diagnosis. METHODS: A case-case design was employed to compare First Nations (FN) women (n = 287) to a frequency-matched random sample of non-FN women (n = 671) diagnosed with breast cancer within the Ontario Cancer Registry. Women were matched on period of diagnosis (1995-1999 and 2000-2004), age at diagnosis (<50 vs. ≥50), and Regional Cancer Centre (RCC). Stage and other factors were collected from medical charts at the RCCs. Survival was compared using an adjusted Cox proportional hazards model and stratified by stage at diagnosis (I, II, and III-IV). Determinants of survival in FN women stratified by stage at diagnosis were also modeled. RESULTS: Survival was more than three times poorer for FN women diagnosed at stage I than for non-FN women (HR = 3.10, 95% CI = 1.39-6.88). The risk of death after a stage I breast cancer diagnosis was about five times higher among FN women with a comorbidity other than diabetes (HR = 4.65, 95% CI = 1.39-15.53) and was more than five times greater for women with diabetes (HR = 5.49, 95% CI = 1.69-17.90) than for those without a comorbidity. CONCLUSIONS: Having a preexisting comorbidity was the most important factor in explaining the observed survival disparity among FN women. IMPACT: Improving the general health status of FN women could increase their survival after an early-stage breast cancer diagnosis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".