Physical activity and the risk of ovarian cancer: A case‐control study in Canada
Bibliographic record
Abstract
We evaluated the impact of recreational and occupational physical activity on ovarian cancer risk using data from a population-based case-control study of 442 cases with histologically confirmed incident ovarian cancer and 2,135 controls aged 20-76 years, conducted in 1994-1997 in Canada. Frequency and intensity of physical activity were collected through self-administered questionnaires. Compared to women in the lowest tertiles of moderate, vigorous and total recreational activity, those in the highest tertiles had multivariable-adjusted odds ratios (and 95% confidence intervals) of 0.67 (0.50-0.88), 0.93 (0.70-1.24) and 0.73 (0.58-0.98), respectively. There were statistically significant trends of decreasing risk with increasing levels of moderate and total recreational activity, with similar patterns for premenopausal and postmenopausal women. A significant reduction in risk associated with higher level of moderate recreational activity was observed for serous, endometrioid and other but not mucinous types of tumors. The analyses in one province with the largest number of cases and controls indicated that occupational activity was associated with reduced ovarian cancer risk by lifetime activity and by various life periods (early 20s, early 30s, early 50s and 2 years before interview). Our study suggests that occupational and regular moderate recreational physical activity reduce ovarian 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".