Lung function and respiratory symptoms in a randomized smoking cessation trial of electronic cigarettes
Bibliographic record
Abstract
Quitting smoking is the most important step smokers can take to improve their health. Nonetheless, there is little information on long-term improvements in lung function and/or respiratory symptoms after smoking cessation. Here we illustrate long-term changes in spirometric indices as well as in respiratory symptoms in smokers invited to quit or reduce their cigarette consumption by switching to electronic cigarettes (ECs). Prospective evaluation of cigarette consumption, spirometry and symptoms was performed in a 1-year randomized controlled trial of smokers receiving EC containing 2.4%, 1.8% or 0% nicotine. Spirometric data are presented on the basis of participants' pooled continuous smoking phenotype classification (Quitters, Reducers, Failures), whereas respiratory symptoms on the basis of their point prevalence-smoking phenotype. Smoking phenotype classification (Quitters, Reducers, Failures) had no significant effect on spirometric indices (FEV1, FVC and FEV1/FVC) with the exception of FEF25-75%, which significantly (P =0.034) increased over the time among Quitters; their FEF25-75% (% predicted) improving from (means±S.D.) 85.7±15.6% at baseline (BL) to 100.8±14.6%. High prevalence of cough/phlegm (43.1%) and shortness of breath (SoB; 34.8%) was reported at BL with substantial reduction in their frequency at subsequent follow-up visits. These symptoms virtually disappeared very quickly in both quitters and reducers. Smokers invited to switch to ECs who completely abstained from smoking showed steady progressive improvements in their FEF25-75% Normalization of peripheral airways function was associated with improvement in respiratory symptoms, adding to the notion that abstaining from smoking can reverse tobacco harm in the lung.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".