A Population-Based Study of Ethnicity and Breast Cancer Stage at Diagnosis in Ontario
Bibliographic record
Abstract
BACKGROUND: Breast cancer stage at diagnosis is an important predictor of survival. Our goal was to compare breast cancer stage at diagnosis (by American Joint Committee on Cancer criteria) in Chinese and South Asian women with stage at diagnosis in the remaining general population in Ontario. METHODS: We used the Ontario population-based cancer registry to identify all women diagnosed with breast cancer during 2005-2010, and we applied a validated surname algorithm to identify South Asian and Chinese women. We used logistic regression to compare, for Chinese or South Asian women and for the remaining general population, the frequency of diagnoses at stage ii compared with stage i and stages ii-iv compared with stage i. RESULTS: The registry search identified 1304 Chinese women, 705 South Asian women, and 39,287 women in the remaining general population. The Chinese and South Asian populations were younger than the remaining population (mean: 54, 57, and 61 years respectively). Adjusted for age, South Asian women were more often diagnosed with breast cancer at stage ii than at stage i [odds ratio (or): 1.28; 95% confidence interval (ci): 1.08 to 1.51] or at stages ii-iv than at stage i (or: 1.27; 95% ci: 1.08 to 1.48); Chinese women were less likely to be diagnosed at stage ii than at stage i (or: 0.82; 95% ci: 0.72 to 0.92) or at stages ii-iv than at stage i (or: 0.73; 95% ci: 0.65 to 0.82). CONCLUSIONS: Breast cancers were diagnosed at a later stage in South Asian women and at an earlier stage in Chinese women than in the remaining population. A more detailed analysis of ethnocultural factors influencing breast screening uptake, retention, and care-seeking behavior might be needed to help inform and evaluate tailored health promotion activities.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 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".