Sedentary work and the risk of breast cancer in premenopausal and postmenopausal women: a pooled analysis of two case–control studies
Bibliographic record
Abstract
OBJECTIVES: There is limited research on the association between sedentary behaviour and breast cancer risk, particularly whether sedentary behaviour is differentially associated with premenopausal and postmenopausal breast cancer. We pooled data from 2 case-control studies from Australia and Canada to investigate this association. METHODS: This pooled analysis included 1762 incident breast cancer cases and 2532 controls. Participants in both studies completed a lifetime occupational history and self-rated occupational physical activity level. A job-exposure matrix (JEM) was also applied to job titles to assess sedentary work. Logistic regression analyses (6 pooled and 12 study-specific) were conducted to estimate associations between both self-reported and JEM-assessed sedentary work and breast cancer risk among premenopausal and postmenopausal women. RESULTS: No association was observed in the 6 pooled analyses, and 10 of the study-specific analyses also showed null results. 2 study-specific analyses provided inconsistent and contradictory results, with 1 showing statistically significant increased risk of breast cancer for self-reported sedentary work among premenopausal women cancer in the Canadian study, and the other a non-significant inverse association between JEM-assessed sedentary work and breast cancer risk among postmenopausal women in the Australian study. CONCLUSIONS: While a suggestion of increased risk was seen for premenopausal women in the Canadian study when using the self-reported measure, overall this pooled study does not provide evidence that sedentary work is associated with breast cancer risk.
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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 | 0.015 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.015 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".