DOES VAPING IMPAIR LUNG FUNCTION IN YOUNG ADULTS?
Bibliographic record
Abstract
Background: It remains unclear whether the use of electronic cigarettes (e-cigarettes or vaping) impairs lung function. We aimed to compare measures of lung function (spirometry and multiple breath washout) between young adults who vape and age-matched healthy controls. Methods: Healthy participants (18-24 years) without a history of respiratory disease, and age-matched participants who self-reported regular e-cigarette use were recruited. Participants were classified into four exposure groups: no exposure, low (1-2 puffs/hour), moderate (3-4 puffs/hour) and heavy (5+ puffs/hour). The primary outcomes were % predicted FEV1 and lung clearance index (LCI), measured using the Exhalyzer D™. Results: A total 56 participants had paired FEV1 and LCI measurements (24 no exposure, 6 low, 6 moderate, 20 heavy). The vaping group, on average, had 1-5 years of exposure. FEV1 % predicted was similar between the healthy and vaping group (mean difference:0.9% (95%CI -7.78;5.92); whereas ventilation inhomogeneity (LCI) was worse in the vaping group (mean difference 0.27 (95%CI 0.08;0.46)). A dose response was observed across exposure levels for LCI (Figure); the highest exposure group had a 0.4 unit (95%CI 0.1;0.56) higher LCI than the healthy controls. Conclusion: There is a small measurable effect of vaping exposure on ventilation inhomogeneity, suggesting that even in otherwise healthy young adults, vaping begins to cause harmful functional changes in the small airways. erj;64/suppl_68/OA2880/F1 F1 F1
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 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".