It’s Absolutely Relative: The Effect of Age on the BMI–Mortality Relationship in Postmenopausal Women
Bibliographic record
Abstract
OBJECTIVE: The use of relative and absolute effect estimates has important implications for the interpretation of study findings. Likewise, examining additive and multiplicative interaction can lead to differing conclusions about the joint effects of two exposure variables. The aim of this paper is to examine the relationship between BMI and mortality on the relative and absolute scales and investigate interaction between BMI and age. METHODS: Data from 68,132 participants in the Women's Health Initiative (WHI) study were used. The risk ratio and risk difference of BMI on mortality were estimated. A product term was also included to examine interaction between BMI and age on the multiplicative scale, and the relative excess risk of interaction was calculated to measure additive interaction. RESULTS: Results demonstrated that the mortality risk ratio decreased as women aged, but the mortality risk difference increased as women aged. Evidence of additive and multiplicative interaction between age and BMI was found. CONCLUSIONS: In postmenopausal women, the relative mortality risk associated with high BMI decreased with increasing age, but the absolute risk of high BMI increased with increasing age. This indicates the importance of considering the interaction between age and BMI to understand mortality risk in older 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 | 0.012 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".