Impact of electronic cigarette ever use on lung function in adults aged 45–85: a cross-sectional analysis from the Canadian Longitudinal Study on Aging
Bibliographic record
Abstract
OBJECTIVE: To describe the sociodemographic characteristics associated with e-cigarette ever use and to examine the impact of e-cigarette ever use on lung function impairment in an ageing population. DESIGN: A cross-sectional analysis of data from the Canadian Longitudinal Study on Aging. SETTING: A national stratified sample of 44 817 adults living in Canadian provinces. PARTICIPANTS: Respondents included participants aged 45-85 and residing in the community in Canadian provinces. OUTCOME MEASURES: /FVC) appropriate for age, sex, height and ethnicity were used to interpret the severity of lung function impairment. Multinomial logistic regression analysis was used to examine the impact of e-cigarette ever use on obstructive and restrictive lung function impairment. RESULTS: The prevalence of e-cigarette ever use was 6.5% and varied by sociodemographic factors including higher prevalence among individuals younger than 65 years, those with lower education attainment and those with lower annual household income. E-cigarette ever use was associated with 2.10 (95% CI 1.57 to 2.08) times higher odds of obstructive lung function impairment after adjusting for conventional cigarette smoking and other covariates. Individuals with exposure to e-cigarette ever use and 15 or more pack-years had 7.43 (95% CI 5.30 to 10.38) times higher odds for obstructive lung function impairment when compared with non-smokers and non-e-cigarette users after adjusting for covariates. Smokers with 15 or more pack-years had higher odds of restrictive lung function impairment irrespective of e-cigarette ever use. CONCLUSIONS: Ever use of e-cigarettes was found to be associated with obstructive lung function impairment after adjusting for covariates, suggesting that e-cigarette use may be adding to the respiratory and other chronic disease burden in the population.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".