Abstract A123: Serum levels of hormones and breast tissue composition in young women
Bibliographic record
Abstract
Abstract A123 Background Percent mammographic density (PMD) is a strong and heritable risk factor for breast cancer with characteristics that suggest it may be a marker of susceptibility to the disease. We have examined serum hormone levels and other factors associated with breast tissue composition in young women, when susceptibility to breast carcinogens is greatest. Methods In 400 young women aged 15-30 years we obtained quantitative measures of breast water, which reflects fibro-glandular tissue, and breast fat using magnetic resonance (MR), and collected anthropometric and other data. All examinations were performed in the follicular phase of the menstrual cycle and fasting blood samples for hormone assays were obtained on the morning of the MR examination. Results Serum levels of growth hormone (GH) and sex hormone binding globulin (SHBG) were positively associated with percent breast water content in all young women aged 15-30, and the associations remained statistically significant after adjustment for height, weight and other covariates. IGF-I was not associated with any MR breast measures. Serum levels of estradiol, progesterone, and testosterone, were not associated with MR breast measures in all young women, or in those aged 20-30, but did show evidence of positive associations with percent water in those aged 15-19 years. After adjustment for other covariates, interactions of age and serum levels of testosterone (p=0.02) and progesterone (p=0.03) were associated with percent water, and estradiol with total water (p=0.07). Conclusions Serum levels of sex hormones were associated with breast tissue composition in young women aged less than 20, while GH and SHBG were associated with breast tissue composition in all young women. Citation Information: Cancer Prev Res 2008;1(7 Suppl):A123.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".