Mammographic Density, Response to Hormones, and Breast Cancer Risk
Bibliographic record
Abstract
BACKGROUND: Percent mammographic density (PMD) is a strong risk factor for breast cancer that changes in response to changes in hormone exposure. We have examined the magnitude of the association of hormone exposure with PMD according to subsequent breast cancer risk. METHODS: In three case-control studies, with 1,164 patient cases and 1,155 controls nested in cohorts of women screened with mammography, we examined the association of PMD measured in the baseline mammogram with risk of breast cancer in the following 1 to 8 years (mean, 3 years), according to use of oral contraceptives (OCs) in premenopausal women, menopause, and hormone therapy (HT) in postmenopausal women. All statistical comparisons are adjusted for age and other risk factors. RESULTS: In premenopausal women who later developed breast cancer (patient cases), PMD was 5.3% greater in past users of OCs than in nonusers (P = .06). In controls, OC users had 2% less density than nonusers (P = .44; test for interaction P = .06). The difference in PMD between premenopausal and postmenopausal women for patient cases was 8.5% (P < .001) and for controls, 3.9% (P = .01; test for interaction P = .03). In postmenopausal women, PMD was 6% greater in patients who used HT than in never users (P < .001). Controls who used HT had 1.6% greater PMD (P = .26) than never users (test for interaction P = .001). Differences in PMD resulted mainly from differences in the dense area of the mammogram. CONCLUSION: Differences in PMD associated with differences in hormone exposure were greater in women who later developed breast cancer than in controls in each of the hormone exposures examined.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".