Risk factors for breast cancer in postmenopausal Caucasian and Chinese-Canadian women
Bibliographic record
Abstract
INTRODUCTION: Striking differences exist between countries in the incidence of breast cancer. The causes of these differences are unknown, but because incidence rates change in migrants, they are thought to be due to lifestyle rather than genetic differences. The goal of this cross-sectional study was to examine breast cancer risk factors in populations with different risks for breast cancer. METHODS: We compared breast cancer risk factors among three groups of postmenopausal Canadian women at substantially different risk of developing breast cancer - Caucasians (N = 413), Chinese women born in the West or who migrated to the West before age 21 (N = 216), and recent Chinese migrants (N = 421). Information on risk factors and dietary acculturation were collected by telephone interviews using questionnaires, and anthropometric measurements were taken at a home visit. RESULTS: Compared to Caucasians, recent Chinese migrants weighed on average 14 kg less, were 6 cm shorter, had menarche a year later, were more often parous, less often had a family history of breast cancer or a benign breast biopsy, a higher Chinese dietary score, and a lower Western dietary score. For most of these variables, Western born Chinese and early Chinese migrants had values intermediate between those of Caucasians and recent Chinese migrants. We estimated five-year absolute risks for breast cancer using the Gail Model and found that risk estimates in Caucasians would be reduced by only 11% if they had the risk factor profile of recent Chinese migrants for the risk factors in the Gail Model. CONCLUSIONS: Our results suggest that in addition to the risk factors in the Gail Model, there likely are other factors that also contribute to the large difference in breast cancer risk between Canada and China.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".