Breast Cancer Survival in Ontario's First Nations Women: Understanding the Determinants
Bibliographic record
Abstract
This study builds on previous research showing that breast cancer survival is poorer for First Nations (FN) women compared to other Ontario women. Few studies have examined breast cancer survival in Indigenous populations compared to general populations; all of these report poorer survival among Indigenous people. Fewer still have examined potential factors related to the poorer survival, but these often suggest poorer prognosis even after adjustment for them. Study objectives were: to compare the distribution of demographic, prognostic and treatment factors between FN and non-FN women; to investigate factors associated with later diagnosis in FN women; to compare stage specific survival for FN and non-FN women controlling for important factors potentially associated with breast cancer survival; and to examine potential determinants of survival for FN women by stage at diagnosis. A case-case design was employed to compare FN women (n=287) diagnosed with invasive breast cancer to a frequency-matched random sample of women (n=671) from the general population diagnosed with breast cancer within the Ontario Cancer Registry. Women were matched (2:1) on period of diagnosis (1995-1999 and 2000-2004), age at diagnosis (<50 vs. 50≥), and Regional Cancer Centre (RCC). Stage at diagnosis and data relevant to the determinants of breast cancer survival were collected from medical charts at the RCCs. FN women were diagnosed with breast cancer at later stages compared to non-FN women. Having a non-screened method of detection and increasing BMI were associated with a later breast cancer diagnosis. FN women with comorbidity however were less likely to be diagnosed at a later stage. An unforeseen novel finding was that the survival disadvantage occurred after an early breast cancer diagnosis, whereas the survival experiences for those diagnosed at stages II+ were similar. In a multivariate analysis, elevated risk was observed for FN women in stage I, and significant risk was seen in women with comorbidity. These findings are actionable and can be used to improve the prognosis of FN women with breast cancer. It is likely that the same or similar factors are largely responsible for the survival disadvantage observed among Ontario FN people for most other major cancers.
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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 | 0.001 |
| 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".