The Association of Sleep Trouble and Physical Inactivity with Breast Cancer Risk in Nova Scotia: Evidence from the Atlantic PATH Cohort
Bibliographic record
Abstract
Breast cancer is a major public health concern, and modifiable health behaviors such as sleep quality and physical activity may influence risk. This study examined the associations between self-reported sleep trouble, sleep duration, and physical activity with breast cancer incidence in a prospective longitudinal cohort of 10,305 females from Nova Scotia. Breast cancer cases were identified through record linkage to the Nova Scotia Cancer Registry. Multivariable logistic regression models were used to estimate adjusted odds ratios (AORs) and 95% confidence intervals (CIs), accounting for sociodemographic factors, reproductive history, comorbidities, and other health behaviors. Frequent sleep trouble (“all of the time”) was significantly associated with increased odds of breast cancer (AOR = 2.41, 95% CI = 1.09–5.34, p = 0.03), while no significant associations were observed between sleep duration and breast cancer risk. High physical activity was significantly associated with a lower risk of breast cancer (AOR = 0.58, 95% CI = 0.39–0.86, p < 0.01). These findings suggest that frequent sleep disturbances may be associated with an increased risk of breast cancer, while high physical activity appears to be linked to a lower risk of breast cancer. Further research is needed to explore these relationships and their underlying mechanisms.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".