A Decade of Breast Cancer in Ontario: Survival Differences Between First Nations and Non-First Nations Women
Bibliographic record
Abstract
Background: Breast cancer is the most common cancer among women in Canada, with rising incidence in First Nations (FN) women. This study confirms persistent survival disparities between FN and non-FN women, with updated findings over an extended follow-up period. Methods: Data from the Ontario Cancer Registry, linked to the Indian Registration System, included 282 FN and 670 frequency matched non-FN women diagnosed with invasive breast cancer from 1995-2004. Survival outcomes were assessed up to 2019, analyzing 10-year survival rates by cancer stage and other factors. Results: Unadjusted Kaplan-Meier curves showed lower 10-year survival for FN women (76%) compared to non-FN women (87%) for stage I breast cancer. After adjusting for age, comorbidity, detection method, and surgery severity, no significant difference in mortality hazard was found. Among FN women diagnosed with stage I breast cancer, diabetes (HR = 3.10, 95% CI = 1.09-8.81) and chemotherapy (HR = 4.05, 95% CI = 1.46-11.20) were associated with increased mortality hazard, while hormonal therapy was associated with reduced mortality hazard (HR = 0.38, 95% CI = 0.14-0.996). Among FN women with advanced-stage breast cancer (stages II-IV), diabetes (HR = 1.74, 95% CI = 1.02-2.98) and radiotherapy (HR = 1.88, 95% CI = 1.07-3.29) were associated with increased mortality hazard. Conclusion: Disparities in survival rates between FN and non-FN women may be influenced by factors such as age, comorbidity, detection method, and surgery type. This study shows encouraging improvement over time and advocates for actionable changes to close the survival gap and enhance treatment outcomes for FN women.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".