Estimating Effect of Obesity on Stroke Using G‐Estimation: The ARIC study
Bibliographic record
Abstract
OBJECTIVE: This study quantified the obesity-stroke relationship by appropriately adjusting for time-varying confounders using G-estimation. METHODS: ; abdominal obesity (AOB) was defined as waist circumference ≥ 102 cm in men and ≥ 88 cm in women and waist to hip ratio ≥ 0.9 in men and ≥ 0.85 in women. The effects of obesity on stroke were estimated using G-estimation and compared with accelerated failure time models using three modeling strategies. RESULTS: The first accelerated failure time model adjusted for baseline covariates excluding metabolic mediators of obesity showed increased risk of stroke for all measures of obesity. Further adjustment for hypertension, diabetes mellitus, and lipid profiles resulted in decreasing hazard ratios (HRs) with intervals that included the null value for all measures of obesity. G-estimated HRs were 1.60 (95% CI: 1.08-2.40), 1.43 (95% CI: 1.14-1.99), and 1.99 (95% CI: 1.50-2.91) for GOB and AOB based on waist circumference and waist to hip ratio. CONCLUSIONS: Both GOB and AOB affected the risk of stroke. The magnitude of the estimates was larger when modeled by G-estimation than when using standard models, suggesting that bias from mishandling of time-varying confounding was toward the null.
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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 | 0.031 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| 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) | 0.001 | 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".