Family History, Mammographic Density, and Risk of Breast Cancer
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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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".