Relation between intensity of physical activity and breast cancer risk reduction
Bibliographic record
Abstract
PURPOSE: To examine the influence of frequency, duration, and intensity of physical activity on risk of breast cancer and to compare breast cancer risks associated with self-reported versus assigned intensity levels of activity. METHODS: A population-based case-control study of 1233 incident breast cancer cases and 1241 controls was conducted in Alberta between 1995 and 1997. The frequency, duration and intensity of occupational, household, and recreational activities were measured throughout lifetime using the Lifetime Total Physical Activity Questionnaire and cognitive interviewing methods. Unconditional logistic regression analyses were used to estimate odds ratios and a full assessment of confounding and effect modification was undertaken. Odds ratios for self-reported and compendium-based assigned levels of activity were compared for lifetime total activity and by type of activity. RESULTS: Breast cancer risk reductions were comparable when self-reported and assigned intensity values were used, although the results and trends were more evident with the assigned intensity data. Moderate-intensity occupational and household activities decreased breast cancer risk, whereas recreational activity, at any intensity level, did not contribute to a breast cancer risk reduction. CONCLUSION: This study found that moderate-intensity activities were the major contributors to the decrease in breast cancer risk found in this study and that risk reductions were more evident when the frequency and duration of activity alone were modeled. Of the three types of activity considered, the greatest risk reductions observed were for occupational and household activities.
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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) | 0.000 | 0.000 |
| 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".