Inhalation Toxicology of Vaping Products and Implications for Pulmonary Health
Bibliographic record
Abstract
E-cigarettes have a liquid that may contain flavors, solvents, and nicotine. Heating this liquid generates an aerosol that is inhaled into the lungs in a process commonly referred to as vaping. E-cigarette devices can also contain cannabis-based products including tetrahydrocannabinol (THC), the psychoactive component of cannabis (marijuana). E-cigarette use has rapidly increased among current and former smokers as well as youth who have never smoked. The long-term health effects are unknown, and emerging preclinical and clinical studies suggest that e-cigarettes may not be harmless and can cause cellular alterations analogous to traditional tobacco smoke. Here, we review the historical context and the components of e-cigarettes and discuss toxicological similarities and differences between cigarette smoke and e-cigarette aerosol, with specific reference to adverse respiratory outcomes. Finally, we outline possible clinical disorders associated with vaping on pulmonary health and the recent escalation of acute lung injuries, which led to the declaration of the vaping product use-associated lung injury (EVALI) outbreak. It is clear there is much about vaping that is not understood. Consequently, until more is known about the health effects of vaping, individual factors that need to be taken into consideration include age, current and prior use of combustible tobacco products, and whether the user has preexisting lung conditions such as asthma and chronic obstructive pulmonary disease (COPD).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".