Physical activity and breast cancer risk: impact of timing, type and dose of activity and population subgroup effects
Bibliographic record
Abstract
OBJECTIVE: To review (1) the epidemiological literature on physical activity and the risk of breast cancer, examining the effect of the different parameters of activity and effect modification within different population subgroups; and (2) the biological mechanisms whereby physical activity may influence the risk of breast cancer. METHODS: A review of all published literature to September 2007 was conducted using online databases; 34 case-control and 28 cohort studies were included. The impact of the different parameters of physical activity on the association between activity and the risk of breast cancer was examined by considering the type of activity performed, the timing of activity over the life course and the intensity of activity. Effect modification of this association by menopausal status, body mass index (BMI), racial group, family history of breast cancer, hormone receptor status, energy intake and parity were also considered. RESULTS: Evidence for a risk reduction associated with increased physical activity was found in 47 (76%) of 62 studies included in this review with an average risk decrease of 25-30%. A dose-response effect existed in 28 of 33 studies. Stronger decreases in risk were observed for recreational activity, lifetime or later life activity, vigorous activity, among postmenopausal women, women with normal BMI, non-white racial groups, those with hormone receptor negative tumours, women without a family history of breast cancer and parous women. CONCLUSIONS: The effect of physical activity on the risk of breast cancer is stronger in specific population subgroups and for certain parameters of activity that need to be further explored in future intervention trials.
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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 | 0.009 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".