Pooled Analysis of Prospective Cohort Studies on Height, Weight, and Breast Cancer Risk
Bibliographic record
Abstract
The association between anthropometric indices and the risk of breast cancer was analyzed using pooled data from seven prospective cohort studies. Together, these cohorts comprise 337,819 women and 4,385 incident invasive breast cancer cases. In multivariate analyses controlling for reproductive, dietary, and other risk factors, the pooled relative risk (RR) of breast cancer per height increment of 5 cm was 1.02 (95% confidence interval (CI): 0.96, 1.10) in premenopausal women and 1.07 (95% CI: 1.03, 1.12) in postmenopausal women. Body mass index (BMI) showed significant inverse and positive associations with breast cancer among pre- and postmenopausal women, respectively; these associations were nonlinear. Compared with premenopausal women with a BMI of less than 21 kg/m2, women with a BMI exceeding 31 kg/m2 had an RR of 0.54 (95% CI: 0.34, 0.85). In postmenopausal women, the RRs did not increase further when BMI exceeded 28 kg/m2; the RR for these women was 1.26 (95% CI: 1.09, 1.46). The authors found little evidence for interaction with other breast cancer risk factors. Their data indicate that height is an independent risk factor for postmenopausal breast cancer; in premenopausal women, this relation is less clear. The association between BMI and breast cancer varies by menopausal status. Weight control may reduce the risk among postmenopausal 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 | 0.033 | 0.050 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.021 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 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".