Appropriate Treatment for Stage 1 and 2 Her2-Positive and Triple-Negative Breast Cancer by Immigration Status in Ontario, Canada
Bibliographic record
Abstract
PURPOSE: This study explored appropriate treatment received for stage 1 and 2 Her2-positive and triple-negative (TN) breast cancer (BC) among immigrants and long-term residents. METHODS: We identified women aged 18- 75 years diagnosed with BC in Ontario from 2012 to 2019. We stratified them into immigrants and long-term residents using the Immigration, Refugee, and Citizenship Canada Permanent Resident database (CIC). We linked to population-wide treatment databases to extract information on breast surgery, chemotherapy, and radiotherapy. We categorized them into 4 mutually exclusive groups based on subtype (Her-2 positive or TNBC) and breast surgery (breast-conserving surgery (BCS) vs. mastectomy). Appropriate treatment included chemotherapy for all (plus Herceptin if Her-2 overexpressing), plus breast radiation therapy if breast-conserving surgery was performed. We could not assess the receipt of endocrine therapy for the hormone receptor-positive subset of Her-2 overexpressors, or indications for postmastectomy radiation therapy. Odds ratios for receiving appropriate treatment were calculated using logistic regression, adjusting for age, resource utilization and area-level residential ethnicity concentration. RESULTS: Crude and univariate analyses showed no differences in the receipt of appropriate treatment. Similarly, adjusted analyses in each of the 4 subgroups showed no difference between immigrants and long-term residents. Among Her2-positive treated by(BCS) group,(Odds Ratio[OR] = 0.82, 95% Confidence Interval[CI] 0.65-1.03, and treated by mastectomy, OR = 0.95 (95% CI, 0.67-1.35). Among TNBC treated by BCS, OR = 0.81 (95% CI, 0.58-1.13), and treated by mastectomy,OR 0.85 (95% CI, 0.49-1.46). CONCLUSION: Immigration status was not associated with the receipt of appropriate treatment amongst early-stage Her2-positive or TNBC breast cancer in Ontario.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".