Scaling of Weight for Height in Relation to Risk of Cancer at Different Sites in a Cohort of Canadian Women
Bibliographic record
Abstract
Many studies have examined the associations of body mass index (weight (kg)/height (m)(2)) with risk of various cancers. However, optimal scaling of weight for height may depend on the population studied. The authors used data from a large cohort study of women (Canadian National Breast Cancer Screening Study, 1980-2000; n = 89,835) to examine how the scaling of weight for height (W/H(x)) influenced the association with risk of 19 different cancers. Cox proportional hazards models were used to estimate the hazard ratio for each cancer site with W/H(x), with x increasing from 0 to 3.0 by increments of 0.1. The correlation between weight and W/H(x) decreased monotonically with increasing x, whereas W/H(x) was minimally correlated with height when x = 1.4. W/H(x) showed significant positive associations with postmenopausal breast cancer, endometrial cancer, kidney cancer, and lung cancer in never smokers. W/H(x) was inversely associated with lung cancer in ever smokers. The value of x for which W/H(x) produced the largest statistically significant hazard ratio ranged from 0.8 (endometrial cancer) to 1.7 (postmenopausal breast cancer). For lung cancer in ever smokers, the inverse association was statistically significant for all values of x. These findings suggest that the scaling of weight for height may vary depending on the cancer site and that optimal scaling may be considerably different from W/H(2) or, alternatively, that a range of scaling should be considered when examining the association of body weight with risk of 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".