Abstract CN13-02: Breast tissue composition and risk of breast cancer
Bibliographic record
Abstract
Abstract CN13-02 Background Susceptibility to breast carcinogens is greatest before the age of 20 years. We have used magnetic resonance (MR) to measure the water content of the breast in young women, and have identified factors associated with variations in breast water. Breast water, like mammographic density, reflects fibro-glandular breast tissue, which is strongly associated with breast cancer risk in middle aged and older women. Methods We recruited 400 young women aged 15-30 years and their mothers, measured breast water and fat in daughters using (MR), and obtained anthropometric and other data. Mothers provided mammograms (n=365) and a random sample (n=100) also had breast MR. Results Percent water by MR in daughters (median=44.8%) was greater than in mothers (median=27.8%) (P<0.0001), and was greater in daughters aged 15-19 years (n=199; median=49%) than in those aged 20-30 years (n=201; median=41%). Percent water in daughters was correlated with both breast water (n=100 pairs; r=0.28; p=0.005), and percent mammographic density in mothers (n=356 pairs; r=0.25, p<0.0001). Percent water in daughters was independently associated with their age (p=0.04) and weight (p<0.0001) (both inversely), and with their height (p<0.0001) and percent density in their mothers’ mammogram (p<0.0001) (both positively). Conclusions Percent breast water was greatest at ages when susceptibility to breast carcinogens is also greatest. The distribution of breast water according to age was consistent with a model in which mammographic density in mid-life is in part the result of genetic influences and growth and development in early life that determine initial breast tissue composition. Citation Information: Cancer Prev Res 2008;1(7 Suppl):CN13-02.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".