A Meta-Analysis of Published Literature on Waist-to-Hip Ratio and Risk of Breast Cancer
Bibliographic record
Abstract
Epidemiological studies have identified body weight as a risk factor for breast cancer. Beyond the amount of adipose tissue a woman has, its distribution, particularly abdominally, may be a risk factor in breast cancer etiology. Body fat distribution is commonly measured by a waist-to-hip circumference ratio lpar;WHR). We performed a meta-analysis to summarize the published literature on WHR and breast cancer risk. After assembling all published studies, we extracted mean WHRs for study participants and adjusted risk estimates comparing highest with lowest partition of WHR and calculated weighted mean differences in WHR between cases and noncases and summary risk estimates based on study design and menopausal status. The weighted mean difference was 0.016 [95% confidence interval (CI) = 0.005-0.028] for all studies combined. The summary risk estimates were 1.80 (95% CI = 1.29-2.50) for case-control studies and 1.27 (95% CI = 1.07-1.51) for cohort studies. By menopausal status, the summary risks were 1.79 (95% CI = 1.22-2.62) for premenopausal women and 1.50 (95% CI = 1.10-2.04) for postmenopausal women. For all studies combined, the summary risk was 1.62 (95% CI = 1.28-2.04). This meta-analysis indicates that a greater WHR is associated with increased risk of breast cancer and suggests that the avoidance of abdominal obesity may reduce risk of the disease.
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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 | 0.018 | 0.043 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.066 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".