Inhalation Toxicology of Vaping Products and Implications for Pulmonary Health
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 it