The association of breast mitogens with mammographic densities
Bibliographic record
Abstract
Radiologically dense breast tissue (mammographic density) is strongly associated with risk of breast cancer, but the biological basis for this association is unknown. In this study we have examined the association of circulating levels of hormones and growth factors with mammographic density. A total of 382 subjects, 193 premenopausal and 189 postmenopausal, without previous breast cancer or current hormone use, were selected in each of five categories of breast density from mammography units. Risk factor information, anthropometric measures, and blood samples were obtained, and oestradiol, progesterone, sex hormone binding globulin, growth hormone, insulin-like growth factor-I and its principal binding protein, and prolactin measured. Mammograms were digitised and measured using a computer-assisted method. After adjustment for other risk factors, we found in premenopausal women that serum insulin-like growth factor-I levels, and in postmenopausal women, serum levels of prolactin, were both significantly and positively associated with per cent density. Total oestradiol and progesterone levels were unrelated to per cent density in both groups. In postmenopausal women, free oestradiol (negatively), and sex hormone binding globulin (positively), were significantly related to per cent density. These data show an association between blood levels of breast mitogens and mammographic density, and suggest a biological basis for the associated risk of breast cancer.
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".