Association between e-cigarette exposure and ventilation homogeneity in young adults: A cross-sectional study
Bibliographic record
Abstract
Background The number of young people who use electronic cigarettes (e-cigarettes) is rising. It remains unclear whether e-cigarettes use impairs lung function. We aimed to compare ventilation distribution between young adults exposed to e-cigarettes with an unexposed group. Methods Study participants included otherwise healthy young adults (18 to 24 years) who self-reported e-cigarette use, and participates who had no history of e-cigarette, tobacco or cannabis exposure. Exposure to e-cigarettes was defined using three measures 1) ever exposed, 2) daily use, and 3) puff frequency, which includes: none (unexposed), low (1–2 puffs/hour), moderate (3–4 puffs/hour) and heavy (5+ puffs/hour). Ventilation distribution was measured using the multiple breath washout test and reported as lung clearance index (LCI). Results A total of 93 participants were recruited, 38 unexposed and 41 exposed participants had LCI measures. The exposed group consisted predominately of participants who used flavoured e-liquids (94.5%) that contained nicotine (93.5%). The magnitude and direction of the difference in LCI across the exposure definitions was similar. Compared with the unexposed group, in the unadjusted models LCI was higher in those with any e-cigarette use (mean difference 0.16 units; 95% CI 0.004; 0.31), daily users (mean difference 0.11; 95% CI 0.06; 0.28) and heavy users (mean difference 0.22; 95% CI 0.03; 0.41). Conclusion This preliminary work suggests that LCI may be a useful biomarker to measure the effects of e-cigarette use on ventilation distribution and to track early functional impairment of the small airways.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".