Breast Cancer Characteristics and Outcomes in Canadian Black Women by Ancestry
Bibliographic record
Abstract
Breast cancer is the most common cancer among women in Canada. Its presentation and outcomes vary significantly by race/ethnicity. This study explores breast cancer incidence, age at diagnosis, stage, subtype, and mortality, comparing Black and White women aged 20 years and older, using the 2011 and 2016 Canadian Census Health and Environment Cohorts databases. Black women were disaggregated into Caribbean, Central/West African (C/WA), Southern/East African (S/EA), and “Other” ancestry groups. The Black female study population had a lower mean age (43.0 years) than the White (50.5 years). Black women had lower overall age-standardized breast cancer incidence than White women. The age-specific incidence in Black women ages 30–39 of Caribbean origin was higher (RR 95% CL, 1.36, 1.04–1.79; 58.7 vs. 43.1 cases/100,000 person-years) than in White. White women had 14.6% of cases diagnosed at ages 20–49 compared to over 50% in Black women of C/WA and S/EA origins, with highest proportions of diagnoses occurring at least 10 years earlier among Black women (C/WA 46, S/EA 48, Caribbean 57, White 67). Proportions of prognostic stage I diagnoses were less common among Black vs. White women (53.2% vs. 65.9%, p < 0.0001), and triple negative breast cancer was more frequent among Black women (17.1% vs. 9.9%, p < 0.0001), particularly those of Central/West African ancestry (21.8%). Higher age-specific mortality was observed among Black women with Caribbean origins aged 40–49 (RR 95% CL, 1.70, 1.19–2.42) and 50–59 (RR 95% CL, 1.42, 1.08–1.88) compared to White women. Breast cancer characteristics and outcomes vary substantially by ancestry within Canada’s Black population. Tailored screening strategies accounting for earlier onset and aggressive subtypes may help mitigate disparities.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".