Family History, Mammographic Density, and Risk of Breast Cancer
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE: Mammographic density is a strong and highly heritable risk factor for breast cancer. The purpose of this study was to examine the extent to which mammographic density explains the association of family history of breast cancer with risk of the disease. SUBJECTS AND METHODS: We carried out three nested case-control studies in screening programs that included in total 2,322 subjects (1,164 cases and 1,158 controls). We estimated the independent and combined associations of family history and percent mammographic density at baseline with subsequent breast cancer risk. RESULTS: After adjustment for age and other risk factors, compared with women with no affected first-degree relatives, percent mammographic density was 3.1% greater for women with one affected first-degree relative, and 7.0% greater for women with two or more affected relatives (P = 0.001 for linear trend across family history categories). The odds ratios for breast cancer risk were 1.37 [95% confidence interval (95% CI), 1.10-1.72] for having one affected relative, and 2.45 (95% CI, 1.30-4.62) for having two or more affected relatives (P for trend = 0.0002). Adjustment for percent mammographic density reduced these odds ratios by 16% and 14%, respectively. Percent mammographic density explained 14% (95% CI, 4-39%) of the association of family history (at least one affected first-degree relative) with breast cancer risk. CONCLUSIONS: Percent mammographic density has features of an intermediate marker for breast cancer, and some of the genes that explain variation in percent mammographic density might be associated with familial risk of breast cancer.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 it