Case‐control study of anthropometric measures and breast cancer risk
Bibliographic record
Abstract
A population-based case-control study of 1,233 incident breast cancer cases and 1,241 controls was conducted in Alberta between 1995 and 1997 to examine the influence of anthropometric factors on the risk of breast cancer using several newly derived variables. Data on current height, weight and waist and hip circumference were collected by interviewers using standardized methods. Respondents recalled their body weight at each decade from age 20 to the referent year. Several variables were estimated, and unconditional logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (95% CIs). No statistically significant associations for any of the estimated variables with breast cancer risk for premenopausal women (462 cases, 475 controls) were found. The results for postmenopausal women (771 cases, 762 controls) in the highest vs. lowest quartiles were, for waist circumference, OR = 1.30 (95% CI 0.97-1.73); waist-hip ratio, OR = 1.43 (95% CI 1.07-1.93); weight gain since age 20, OR = 1.35 (05% CI 1.01-1.81); difference between maximum and minimum weights over adult lifetime, OR = 1.56 (95% CI 1.16-2.08); and the reference weight minus the minimum weight since age 20, OR = 1.47 (95% CI 1.10-1.97). Statistically significant trends in risk were observed for these variables. Effect modification with hormone replacement therapy use was found for most variables assessed for postmenopausal women, with much stronger associations found among never-users compared to ever-users. We found strong evidence that waist-hip ratio and weight gained over lifetime, as assessed by different variables, are postmenopausal breast cancer risk factors. These effects were independent of dietary intake and lifetime total physical activity.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".