Smoking is Associated With Impaired Long-term Quality of Life in Elderly People: A 22-year Cohort Study in NIPPON-DATA 90
Bibliographic record
Abstract
BACKGROUND: Whether smoking is associated with worse quality of life (QoL) or not is relatively controversial. The current study is to investigate the relationship between smoking and subjective QoL in a long cohort study. METHODS: The NIPPON DATA 90 project collected 8,383 community residents in 300 randomly selected areas as baseline data in 1990, administered four follow-up QoL surveys, and evaluated mortality statistics. We conducted multinomial logistic regression analysis to compare past smokers and current smokers to never smokers, with impaired QoL and mortality as outcomes. RESULTS: In four follow-ups, QoL data was collected from 2,035, 2,252, 2,522, and 3,280 participants in 1995, 2000, 2005, and 2012, respectively. In the 1995 follow-up, current smoking at baseline was not associated with worse QoL. In 2000 and 2005 follow-ups, smoking was significantly associated with worse QoL (odds ratio [OR] 2.1; 95% confidence interval [CI], 1.33-3.36 and OR 2.29; 95% CI, 1.38-3.80, respectively). In the 2012 follow-up, smoking was not associated with QoL. Sensitivity analysis did not change the result significantly. CONCLUSION: In this study we found that baseline smoking was associated with worse QoL in long-follow-up.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".