Effects Of Electronic Cigarettes On Lung Function
Bibliographic record
Abstract
Electronic cigarettes have become increasingly popular aid in smoking cessation which addresses both nicotine cravings as well as behavioral aspects of smoking. In comparison to the nicotine patches, electronic cigarettes were found equally successful in helping individuals quit smoking for at least six months, however data on any of the systemic effects is lacking. PURPOSE: To investigate the effects of acute electronic cigarette smoking on lung function by measuring the forced vital capacity (FVC), forced expiratory volume after one second (FEV1) and the FEV1/FVC ratio (FEV1%). METHODS: Fourteen regular smokers, between the ages 19 and 27 without any preexisting medical conditions were randomized into treatment and control group. All fourteen participants performed a baseline spirometric measurement (FVC, FEV1, FEV1%) followed by the ten inhalations from an electronic cigarette charged with either the nicotinic or non-nicotinic cartridge. After the acute smoking bout, all participants performed a second spirometric test. Comparison of the post treatment results to the baseline values garnered a percent change in lung function parameters. RESULTS: An acute electronic cigarette smoking caused a decrease in FVC (from 4.28 ± 0.4 to 4.01±0.5 L, n=7; P=0.07), accompanied with a similar decrease in FEV1 (P<0.05). Consequently, an acute electronic cigarette smoking caused a reduction in functional expiratory volume, FEV1%, from 81.9±3.6% to 78.9±3.3% (n=7; p=0.013). CONCLUSIONS: Electronic cigarette smoking reduced functional expiratory volume, indicative of an increased airflow resistance after acute nicotine inhalation.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".