Impact of immigration status on survival amongst early-stage (1&2) HER2 positive and triple-negative breast cancer patients in Ontario, Canada.
Bibliographic record
Abstract
e12512 Background: This study explored death from breast cancer and death from other causes amongst women with stages 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 st, 2012, to Dec 31 st, 2019, and followed them up to 31 st Dec, 2023. We stratified them into immigrants and long-term residents according to the Immigration, Refugee, and Citizenship Canada Permanent Resident (CIC) database. Using linked administrative data sources, we extracted information on the date of diagnosis, molecular subtype, death due to breast cancer, and death due to all other causes. Using competing risks regression (Fine & Gray method), we analyzed the influence of immigration on breast cancer survival, adjusting for age, molecular subtype, stage, resource utilization, and marginalization by ethnic concentration quintile and calculated the sub-distribution hazard ratios (SHR). Results: We included 8,927 women, 1,617 immigrants (540 with TNBC, and 1,077 had her2 positive BC), and 7,310 long-term residents (2,724 with TNBC, and 4586 had her2 positive). The mean age was 55.75 (SD 10.81). Most immigrants lived in areas comprising the highest quintile of ethnic concentration, indicating the highest level of marginalization. At the end of the follow-up period, 557 (6.2%) had died from breast cancer, while 475 (5.3%) had died from all other causes. Conclusions: Immigration status does not increase the risk of death from breast cancer or death from other causes among women with stage 1 and 2 Her 2 positive or triple-negative breast cancer in Ontario. Multivariable Cox proportional hazards regression with sub-distribution hazards. Breast cancer deaths Deaths from other causes p-value SHR 95% ConfidenceLimit p-value SHR 95% Confidence Limit Immigrants vs long-term residents 0.68 1.05 0.82 1.35 0.58 0.92 0.67 1.25 Her2 positive vs TNBC <.0001 0.35 0.30 0.42 0.36 0.92 0.76 1.10 Ethnic concentration quintileQuintile 1 (lowest)(ref)Quintile 2Quintile 3Quintile 4Quintile 5 0.620.330.350.58 1.070.870.880.93 0.820.660.660.70 1.401.151.161.22 0.200.870.010.09 0.830.980.680.77 0.630.740.500.57 1.101.280.911.05 For 1-year increase in age <.0001 1.02 1.01 1.03 <.0001 1.06 1.05 1.08
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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".