Dynamics of smoking cessation and health-related quality of life among Canadians.
Bibliographic record
Abstract
BACKGROUND: People who smoke are at increased risk of lung and other cancers, heart attack, stroke, chronic lung disease and premature death. After smoking cessation, these risks diminish, but little is known about the time required to regain the level of health of people who have never smoked. This analysis describes trajectories of health-related quality of life (HRQL) in relation to smoking status, focusing on the time required for former smokers to achieve an HRQL level similar to that of never-smokers. METHODS: Data were from nine cycles (1994/1995 through 2010/2011) of the National Population Health Survey. Analyses were based on longitudinal data for 3,341 men and 4,143 women aged 40 or older in 1994/1995. Multi-level growth modelling was used to describe HRQL trajectories over the 16-year follow-up period in relation to smoking status, which was updated every two years. RESULTS: Across all ages and for both sexes, persistent smokers had lower HRQL than did never-smokers. Among men, HRQL improved after 5 years of quitting; after 20 years, HRQL was similar to that of never-smokers. Among women, after 10 years of cessation, the HRQL of former smokers was clinically similar to that of those who had never smoked. INTERPRETATION: At any age, and for both men and women, long-term smoking cessation results in improvements in HRQL.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".