Mammographic densities and the prevalence and incidence of histological types of benign breast disease
Bibliographic record
Abstract
There is now a large amount of evidence indicating that women with extensive areas of mammographic densities are 4-6 times more likely to develop breast cancer than those with little or no density in the mammogram. We have examined one potential biological explanation for this association by estimating the incidence of various histological types of benign breast disease in relation to mammographic density. We studied the large cohort of women taking part in the National Breast Screening Study (NBSS), a randomized trial of screening with mammography. Mammograms from subjects with biopsies (n = 423) and from a comparison group of subjects randomly selected from the NBSS (n = 465) were included. Histological slides from biopsied subjects (n = 353) were classified independently by the pathologists of the NBSS and by a review pathologist (H.M.J.). Mammographic density in more than 75% of the breast area was associated with an increased risk of incidence of hyperplasia without atypia, and of atypical hyperplasia and/or carcinoma in situ. The classifications of the review pathologist showed that, compared to women with no density, the relative risk of incident lesions for women with density in more than 75% of breast was 13.85 (95% CI 2.65-72.49) for hyperplasia, and 9.23 (95% CI 1.66-51.48) for atypical hyperplasia and/or carcinoma in situ. These findings suggest that the association between extensive mammographic density and breast cancer risk may, at least in part, be attributable to biological processes in the breast that give rise to these histological features that are known to be related to breast cancer risk.
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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 | 0.010 |
| 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 | 0.000 | 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".