Effect of Physical Activity on Women at Increased Risk of Breast Cancer: Results from the E3N Cohort Study
Bibliographic record
Abstract
PURPOSE: There is a need to investigate the type, duration, frequency, and intensity of physical activity that are critical to reduce the risk of breast cancer, and if this relation differs among subgroups of women. METHODS: We analyzed the relation between physical activity and breast cancer incidence between 1990 and 2002 (n=3,424 cases), among 90,509 women of the French E3N cohort, ages between 40 and 65 years in 1990. We gave special attention to effect modification by body mass index (BMI), family history of breast cancer, parity, and hormone replacement therapy (HRT). RESULTS: A linear decrease in risk of breast cancer was observed with increasing amounts of moderate (P(trend)<0.01) and vigorous (P(trend)<0.0001) recreational activities. Compared with women who reported no recreational activities, those with more than five weekly hours of vigorous recreational activity had a relative risk of 0.62 (0.49-0.78). This decrease was still observed among women who were overweight, nulliparous, had a family history of breast cancer, or used HRT. Compared with the whole cohort, among nulliparous women, the reduction of risk observed was of a higher magnitude, although the test for heterogeneity did not reach statistical significance. CONCLUSION: A risk reduction of breast cancer was particularly observed with vigorous recreational activity. Further investigations are needed to confirm that intensity is an important variable to consider in risk reduction and to identify the precise biological mechanisms involved in such a risk reduction.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 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".