Breast cancer incidence and mortality, by age, stage and molecular subtypes, by race/ethnicity in Canada
Bibliographic record
Abstract
BACKGROUND: Breast cancer (BC) characteristics and outcomes in Canada related to race/ethnicity are not currently documented. METHODS: Age-specific and age-standardized BC incidence and mortality rates, age distribution of cases, proportions of stage, and molecular subtypes were calculated for women aged 20+, by race/ethnicity, using 2006 and 2011 Canadian Census Health and Environment Cohort databases of linked census, cancer, and death data. RESULTS: In 47 105 BC cases, age-specific incidence rates were higher in Filipina (rate ratio (RR) = 1.27, 95%CI, 1.11-1.46) and multiethnicity (RR = 1.57, 95% CI, 1.18-2.08) compared to White women aged 40-49; and Filipina (RR = 1.16, 95% CI, 1.02-1.31) and Arab (RR = 1.3, 95% CI, 1.02-1.65) women aged 50-59. Median age at diagnosis was 63 among White women and 52-60 among other race/ethnicity groups, with 22.4%-41.1% of cases (P < .001) diagnosed before age 50 compared to 16.6% among White women. BC was diagnosed at stage I less frequently among Filipina (38.6%), Black (39.2%), South Asian (40.6%), and First Nations (40.7%) compared to White (46.5%) and Chinese (49.6%) (P < .05) women. Black women had higher proportions of BC diagnoses at stages III and IV combined (26.3%) than White women (17.0%, P = .001). The proportion of triple-negative BC among Black women (20.5%) was higher than among White (9.5%, P < .001). Compared to White, age-specific BC mortality rates were higher among Black women aged 40-49 (RR = 1.4, 1.06-1.85) as well as First Nations (RR = 1.21, 1.01-1.45) and Métis (RR = 1.48, 1.15-1.91) women aged 60-69. INTERPRETATION: Compared to White women, other Canadian women had an earlier peak age of BC diagnosis and higher proportions of cases diagnosed under age 50. Although many race/ethnicity groups had lower BC incidence and mortality than White, the higher age-specific BC mortality among Black 40-49 and First Nations and Métis women 60-69 merits further investigation.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".