Impact of Immigration Status on Survival Among Stage 1 and 2 <scp>HER2</scp> ‐Positive and Triple‐Negative Breast Cancer in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: This study examined death from breast cancer and death from other causes among women with Stage 1 and 2 Her2-positive and triple-negative breast cancer (BC) by immigration status. METHODS: We identified women aged 18-75 diagnosed with BC in Ontario from January 1, 2012, to December 31, 2019, followed them to December 31, 2023, and identified legal immigrants from the Immigration, Refugee, and Citizenship Canada Permanent Resident (CIC) database. We linked administrative data sources for the date of diagnosis, molecular subtype, death due to breast cancer, and death due to all other causes. Using adjusted competing risks regression (Fine and Gray method), we analyzed the influence of immigration on breast cancer survival and calculated the sub-distribution hazard ratios (sHR). RESULTS: There was no increased risk of death among legal immigrants on univariate or multivariable analysis. They had a sHR of 0.95 (0.77-1.19) on univariate analysis and 1.06 (95% CI: 0.83-1.36) on multivariable analysis for breast cancer deaths, and for other causes of death, 0.63 (0.47-0.83) on univariate analysis, and 0.85 (95% CI: 0.62-1.15) on multivariable analysis compared to long-term residents. Patients with HER2-positive status had a lower risk of death from breast cancer and other causes compared to those with triple-negative breast cancer (TNBC). Patients with Stage 2 cancer had a significantly higher hazard of death from breast cancer compared to Stage 1 (HR = 3.72, 95% CI: 2.96-4.66, p < 0.0001). CONCLUSIONS: In Ontario, legal immigrants do not have an increased risk of death from breast cancer or other causes compared to long-term residents.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".