Risk Factors for Breast Cancer Associated with Mammographic Features in Singaporean Chinese Women
Bibliographic record
Abstract
BACKGROUND: Mammographic density has been found to be a strong risk factor for breast cancer and to be associated with age, body weight, parity, and menopausal status. Most studies to date have been carried out in Western populations. The purpose of the study described here was to determine in a cross-sectional study in a Singaporean Chinese population the demographic, menstrual, reproductive, and anthropometric factors that are associated with quantitative variations in age-adjusted percentage mammographic densities and to examine the association of these factors with the dense and nondense areas of the mammogram. METHOD: We used mammograms and questionnaire data collected from subjects in the Singapore Breast Screening Project. Women ages 45 to 69 years participated and 84% of those screened were Chinese. Mammograms were digitized and percentage density was measured and analyzed in relation to the questionnaire data. RESULTS: Percentage mammographic density was associated with several risk factors for breast cancer, most of them also associated, in opposite directions, with the dense and nondense components of the image. Percentage density was associated with age and weight (both negatively), height and age at first birth (both positively), and number of births and postmenopausal status (both negatively). Percentage density was weakly associated with a previous breast biopsy but was not associated with age at menarche or menopause, with use of hormones, or with a family history of breast cancer. CONCLUSION: Percentage mammographic density in Singaporean Chinese women has similar associations with risk factors for breast cancer to those seen in Caucasians.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".