Physical Activity in Relation to Mammographic Density in the Dutch Prospect-European Prospective Investigation into Cancer and Nutrition Cohort
Bibliographic record
Abstract
BACKGROUND: Evidence accumulates that physical inactivity is one of the few modifiable risk factors for breast cancer. The mechanism through which physical inactivity affects breast cancer risk is not clear. The study aim was to investigate the association between physical activity and breast density because mammographic density is strongly associated with breast cancer risk. METHODS: We did a cross-sectional study in 620 women, of ages 49 to 68 years and participants of the Dutch Prospect-European Prospective Investigation into Cancer and Nutrition cohort. A self-administered questionnaire was used to obtain information on duration and intensity of physical activity (recreational, household, and occupational) during the year preceding study recruitment. A total activity index (inactive, moderately inactive, moderately active, and active) was estimated by combining all activity types. Percent and absolute breast density were determined on screening mammograms using a computer-aided method. Multivariate linear regression was used to examine the association between physical activity and breast density. RESULTS: Mean percent density was 35.3% [95% confidence interval (95% CI), 31.8-38.8] for the inactive category compared with 36.1% (95% CI, 33.0-39.2) for the active category. Mean absolute density values for the inactive and active category were 45.8 cm(2) (95% CI, 40.9-50.7) and 42.6 cm(2) (95% CI, 38.3-47.0), respectively. Subgroup analysis for postmenopausal women showed similar results, as did separate analyses for recreational and household activity. CONCLUSIONS: The result does not support a relation between current physical activity and mammographic density in postmenopausal women.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".