Mammographic Density as a Surrogate Marker for the Effects of Hormone Therapy on Risk of Breast Cancer
Bibliographic record
Abstract
BACKGROUND: Some types of hormone therapy increase both risk of breast cancer and mammographic density, a risk factor for the disease, suggesting that mammographic density may be a surrogate marker for the effects of hormones on risk of breast cancer. This research was undertaken to determine whether the effect of hormone therapy on breast cancer risk is mediated by its effect on mammographic density. METHODS: Individually matched cases and controls from three nested case-control studies in breast screening populations were studied. Cases had developed invasive breast cancer at least 12 months after the initial screen. Information was collected on hormone use and other risk factors at the time of the baseline mammogram, and percent density was measured by a computer-assisted method. RESULTS: There were 1,748 postmenopausal women, of whom 426 (24.4%) were using hormones at the time of their initial screening mammogram. Current use of hormone therapy was associated with an increased risk of breast cancer (odds ratio, 1.26; 95% confidence interval, 1.0-1.6) that was little changed by adjustment for percent density in the baseline mammogram (odds ratio, 1.19; 95% confidence interval, 0.9-1.5). Percent density in the baseline mammogram was among cases greater in current users of hormones that in never-users (difference = 5.0%, P < 0.001), but the difference was smaller and nonsignificant in controls (difference = 1.6%, P = 0.3). CONCLUSION: Although the effects of hormone therapy on mammographic density were greater in cases than controls, we did not find evidence that these effects were causally related to risk of breast cancer.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 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".