Mammographic Density: A Heritable Risk Factor for Breast Cancer
Bibliographic record
Abstract
The appearance of the breast on mammography varies among women, reflecting variations in tissue composition. Stroma and epithelium attenuate x-rays more than fat and appear light on a mammogram, which we refer to here as " mammographic density, " while fat appears dark. We show evidence that mammographic density is a strong risk factor for breast cancer, and that risk of breast cancer is four to five times greater in women with density in more than 75% of the breast, compared with those with little or no density. Density in more than 50% of the breast may account for a large proportion of breast cancers. Density is influenced by age, parity, body mass index, and menopause but these factors account for only 20 - 30% of the variation in density in the population. Twin studies have shown that percent mammographic density, at a given age, is highly heritable, and that inherited factors explain 63% of the variance. Mammographic density has the characteristics of a quantitative trait, and may be influenced by genes that are easier to identify than those associated with breast cancer itself. The genes that influence mammographic density may also be associated with risk of breast cancer, and their identification is also likely to provide insights into the biology of the breast, and to identify potential targets for preventive strategies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".