Gender Differences in the Relationship Between Smoking and Frailty: Results From the Beijing Longitudinal Study of Aging
Bibliographic record
Abstract
BACKGROUND: Smoking is common in China, where the population is aging rapidly. This study evaluated the relationship between smoking and frailty and their joint association with health and survival in older Chinese men and women. METHODS: Data came from the Beijing Longitudinal Study of Aging, a representative cohort study with a 15-year follow-up. Community-dwelling people (n = 3257) aged more than 55 years at baseline were followed between 1992 and 2007, during which time 51% died. A frailty index (FI) was constructed from 28 self-reported health deficits. RESULTS: Almost half (1,485 people; 45.6%) of the participants reported smoking at baseline (66.8% men, 25.3% women). On average, male smokers were frailer (FI = 0.17±0.13) than male nonsmokers (FI = 0.13±0.10; p = .038). No such differences were seen in women. Men who smoked had the lowest survival probability; female nonsmokers had the highest. Compared with female nonsmokers, the risk of death for male smokers was 1.58 (95% CI = 1.41-1.95; p < .001), adjusted for age and education. Across all FI values, female smokers and male nonsmokers had comparable survival rates. CONCLUSION: Smoking was associated with an increased rate of both worsening health and mortality. At all levels of health status, as defined by deficit accumulation, women who smoked lost the survival advantage conferred by their sex.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".