Anthropometric measures and epithelial ovarian cancer risk in the European Prospective Investigation into Cancer and Nutrition
Bibliographic record
Abstract
We examined the associations of measured anthropometric factors, including general and central adiposity and height, with ovarian cancer risk. We also investigated these associations by menopausal status and for specific histological subtypes. Among 226,798 women in the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort, there were 611 incident cases of primary, malignant, epithelial ovarian cancer diagnosed during a mean 8.9 years of follow-up. Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs), adjusted for potential confounders. Compared to women with body mass index (BMI) < 25 kg/m2, obesity (BMI > or = 30 kg/m2) was associated with excess ovarian cancer risk for all women combined (HR = 1.33, 95% CI = 1.05-1.68; p(trend) = 0.02) and postmenopausal women (HR = 1.59, 95% CI = 1.20-2.10; p(trend) = 0.001), but the association was weaker for premenopausal women (HR = 1.16, 95% CI = 0.65-2.06; p(trend) = 0.65). Neither height or weight gain, nor BMI-adjusted measures of fat distribution assessed by waist circumference, waist-hip ratio (WHR) or hip circumference were associated with overall risk. WHR was related to increased risk of mucinous tumors (BMI-adjusted HR per 0.05 unit increment = 1.17, 95% CI = 1.00-1.38). For all women combined, no other significant associations with risk were observed for specific histological subtypes. This large, prospective study provides evidence that obesity is an important modifiable risk factor for epithelial ovarian cancer, particularly among postmenopausal women.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".