Associations of overall and abdominal adiposity with area and volumetric mammographic measures among postmenopausal women
Bibliographic record
Abstract
Whereas mammographic density and adiposity are positively associated with postmenopausal breast cancer risk, they are inversely associated with one another. To examine the association between these two risk factors, a secondary analysis of data from a randomized controlled trial of a year-long aerobic exercise intervention was done. Participants were 302 postmenopausal women aged 50-74 years. Dense fibroglandular and nondense fatty tissue were measured from mammograms using computer-assisted thresholding software for area measurements and a technique relying on the calibration of mammography machines with a tissue-equivalent phantom for volumetric measurements. Adiposity was measured by anthropometry (body mass index, waist circumference), whole-body dual x-ray absorptiometry scans (body fat) and computed tomography scans (abdominal adiposity). Correlations were estimated between and within women, the latter representing the association between the 1-year change in adiposity and mammographic measures. Adiposity was correlated with nondense area and volume (0.50 ≤ r ≤ 0.66 between women; 0.18 ≤ r ≤ 0.46 within women). Between women, adiposity was correlated with dense area and volume (-0.12 ≤ r ≤ -0.30) and with percent dense area and volume (-0.28 ≤ r ≤ -0.48). Because measurements made with scans explained at most only 3% more of the variation in absolute or percent density beyond that explained by anthropometric measurements, anthropometric measurements are likely sufficient for adjustment of the association between mammographic density and breast cancer risk. Adiposity is associated with breast fatty tissue and possibly weakly inversely associated with fibroglandular tissue.
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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 | 0.003 |
| 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 | 0.000 |
| 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".