Survival Differences in Chinese Versus White Women With Breast Cancer in the United States: A SEER-Based Analysis
Bibliographic record
Abstract
PURPOSE The affect of race on breast cancer prognosis is not well understood. We compared crude and adjusted breast cancer survival rates of Chinese women versus White women in the United States. METHODS We conducted a cohort study of Chinese and White women with breast cancer diagnosed between 2004 to 2015 in the SEER 18 registries database. We abstracted information on age at diagnosis, tumor size, grade, lymph node status, receptor status, surgical treatment, receipt of radiotherapy and chemotherapy, and death. We compared crude breast cancer–specific mortality between the two ethnic groups. We calculated adjusted hazard ratios (HRs) in a propensity-matched design using the Cox proportional hazards model. P < .05 was considered statistically significant. RESULTS There were 7,553 Chinese women (1.8%) and 414,618 White women (98.2%) with stage I-IV breast cancer in the SEER database. There were small differences in demographics, nodal burden, and clinical stage between Chinese and White women. Ten-year breast cancer–specific survival was 88.8% for Chinese women and 85.6% for White women (HR, 0.73; 95% CI, 0.67 to 0.80; P < .0001). In a propensity-matched analysis among women with stage I–IIIC breast cancer, the HR was 0.71 (95% CI, 0.62 to 0.81; P < .0001). Annual mortality rates in White women exceeded those in Chinese women for the first 9 years after diagnosis. CONCLUSION Chinese women in the United States have superior breast cancer–specific survival compared with White women. The reason for the observed difference is not clear. Differences in demographic and tumor features between Chinese and White women with breast cancer may contribute to the disparity, as may the possibility of intrinsic biologic differences.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".