How harmless are E-cigarettes? Effects in the pulmonary system
Bibliographic record
Abstract
PURPOSE OF REVIEW: Electronic cigarettes have quickly risen to be the leading alternative nicotine source to tobacco. E-cigarette use is hard to research and regulate because of the novelty and rapid evolution of the devices and E-liquids. Epidemiological data on long-term usage is currently lacking, but in smaller cohort studies we are starting to understand the usage patterns and demographics of users, which differ depending on where the study takes place and the regulatory environment. The present review describes the current knowledge of the effects of E-cigarettes on the pulmonary system and knowledge of their usage patterns worldwide. RECENT FINDINGS: E-cigarette use is continuing to rise in young adults in United States and Canada, but not in United Kingdom. These suggest that regulation is influencing uptake in young adults. If E-cigarettes are to be considered as a harm minimisation smoking cessation product, use in young never smokers must be factored into the risk assessment. A recent surge in cases of lung injury associated with vaping in America has resulted in the definition of vaping associated pulmonary injury, although the exact cause remains unknown. SUMMARY: It is our opinion that E-cigarettes can no longer be defined as harmless. Further studies are needed to determine the risks for all populations as it is evident that a large proportion of E-cigarette users are never-smokers, meaning they cannot only be considered from a harm reduction perspective.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".