A Population-Based Cross-Sectional Study Comparing Breast Cancer Stage at Diagnosis between Immigrant and Canadian-Born Women in Ontario
Bibliographic record
Abstract
There is limited information on stage at breast cancer diagnosis in Canadian immigrant women. We compared stage at diagnosis between immigrant women and Canadian-born women, and determined whether ethnicity was an independent factor associated with stage. 41,213 women with invasive breast cancer from 2007 to 2012 were identified from the Ontario Cancer Registry. Women were classified as either immigrants or Canadian-born by linkage with the Immigration, Refugees, and Citizenship Canada's Permanent Resident database. Women's ethnicity was classified as Chinese, South Asian, or remaining women in Ontario. Logistic regression was performed to calculate the odds ratio (OR) of being diagnosed at stage I breast cancer (versus stage II-IV). 4,353 (10.6%) women were immigrants and 36,860 (89.4%) were Canadian-born women. The mean age at breast cancer diagnosis was 53.5 years for immigrants versus 62.3 years for Canadian-born women (p < 0.0001). Immigrant women were less likely than Canadian-born women to be diagnosed with stage I breast cancers (adjusted OR = 0.85; 95% CI: 0.79-0.91; p < 0.0001). The adjusted OR of being stage I was 1.28 (95% CI: 1.14-1.43; p < 0.0001) for women of Chinese ethnicity and was 0.82 (95% CI: 0.70-0.96; p = 0.01) for women of South Asian ethnicity, compared to the remaining women in Ontario. Canadian immigrant women were less likely than Canadian-born women to be diagnosed with early-stage breast cancers. Ethnicity was a greater contributor to the stage disparity than was immigrant status. South Asian women, regardless of immigration status, might benefit from increased breast cancer awareness programs.
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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 | 0.001 |
| 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) | 0.001 | 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".