Effect of smoking on the accumulation of deficits, frailty and survival in older adults: A secondary analysis from the Canadian study of health and aging
Bibliographic record
Abstract
OBJECTIVES: Smoking has adverse effects on a variety of organ systems but little is known about the relationship between smoking and frailty. We aimed to investigate differences in health status between smoking and non smoking older adults. DESIGN AND SETTING: The Canadian Study of Health and Aging, a nationally representative cohort study. PARTICIPANTS: Nine thousand and eight community-dwelling men and women age 65 years and over at baseline. MEASUREMENTS: Smoking status was determined using a Self-Assessed Risk Factor Questionnaire. Comparisons were made between never smokers, light smokers and heavy smokers with heavy smokers defined as those who smoked >or= 1 pack per day for 20 years or more. A frailty index (FI) generated from 40 self-reported health deficits was also modified to exclude 5 variables that could be directly attributed to smoking (e.g. cough). Decedent information was collected over 10 years. RESULTS: Average FI values increased exponentially with age. For both men and women, heavy smokers were the most frail, light smokers had intermediate frailty status and never smokers were fittest. Modification of the FI did not impact these differences. Heavy smokers had significantly worse mortality than non smokers and higher rates of death in smokers persisted in the oldest old. 120 month survival curves, grouped for age, sex and smoking status showed that male smokers > 75 years had the highest mortality rates. CONCLUSIONS: Smoking causes poorer health status at older ages which can be captured by the frailty index. Higher rates of death in smokers persist in the oldest old, with no emergence of "survivors" with fitness or longevity advantages.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".