Height, Body Mass Index, and Ovarian Cancer: A Pooled Analysis of 12 Cohort Studies
Bibliographic record
Abstract
BACKGROUND: Although many studies have investigated the association between anthropometry and ovarian cancer risk, results have been inconsistent. METHODS: The associations of height, body mass index (BMI), and ovarian cancer risk were examined in a pooled analysis of primary data from 12 prospective cohort studies from North America and Europe. The study population consisted of 531,583 women among whom 2,036 epithelial ovarian cancer cases were identified. To summarize associations, study-specific relative risks (RR) were estimated using the Cox proportional hazards model and then combined using a random-effects model. RESULTS: Women with height > or =1.70 m had a pooled multivariate RR of 1.38 [95% confidence interval (95% CI), 1.16-1.65] compared with those with height <1.60 m. For the same comparison, multivariate RRs were 1.79 (95% CI, 1.07-3.00) for premenopausal and 1.25 (95% CI, 1.04-1.49) for postmenopausal ovarian cancer (P(interaction) = 0.14). The multivariate RR for women with a BMI > or =30 kg/m(2) was 1.03 (95% CI, 0.86-1.22) compared with women with a BMI from 18.5 to 23 kg/m(2). For the same comparison, multivariate RRs were 1.72 (95% CI, 1.02-2.89) for premenopausal and 1.07 (95% CI, 0.87-1.33) for postmenopausal women (P(interaction) = 0.07). There was no statistically significant heterogeneity between studies with respect to height or BMI. BMI in early adulthood was not associated with ovarian cancer risk. CONCLUSION: Height was associated with an increased ovarian cancer risk, especially in premenopausal women. BMI was not associated with ovarian cancer risk in postmenopausal women but was positively associated with risk in premenopausal women.
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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 | 0.022 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.021 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".