Underweight rather than adiposity is an important predictor of death in rural Chinese adults: a cohort study
Bibliographic record
Abstract
Background To assess the associations of body mass index (BMI) with all-cause and cause-specific mortalities among rural Chinese. Methods A prospective study of 28 895 individuals was conducted from 2006 to 2014 in rural Deqing, China. Height and weight were measured. The association of BMI with mortality was assessed by using Cox proportional hazards model and restricted cubic spline regression. Results There were a total of 2062 deaths during an average follow-up of 7 years. As compared with those with BMI of 22.0–24.9 kg/m2, an increased risk of all-cause mortality was found for both underweight men (BMI <18.5 kg/m2) (adjusted HR (aHR): 1.45, 95% CI: 1.18 to 1.79) and low normal weight men (BMI of 18.5–21.9 kg/m2) (aHR: 1.20, 95% CI: 1.03 to 1.38). A J-shaped association was observed between BMI and all-cause mortality in men. Underweight also had an increased risk of cardiovascular disease and cancer mortalities in men. The association of underweight with all-cause mortality was more pronounced in ever smokers and older men (60+ years). The results remained after excluding participants who were followed up less than 1 year. Conclusion The present study suggests that underweight is an important predictor of mortality, especially for elderly men in the rural community of China.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".