Electronic nicotine delivery system design and aerosol toxicants: A systematic review
Bibliographic record
Abstract
BACKGROUND: Electronic nicotine delivery systems (ENDS; e-cigarettes), consisting of a battery, heating element and e-liquid, have evolved significantly with wide variation in design, components, operating powers, and chemical constituents. Generated aerosols have been reported to contain potentially toxic substances. We conducted a systematic review to assess what is known about the presence of toxicants in ENDS aerosols in order to inform how system design could mitigate risk. METHODS: Articles reporting on or evaluating design characteristics of ENDS and aerosol constituents were included and summarized. RESULTS: The search identified 2,305 articles, of which 92 were included after full-text review. Findings were grouped into 6 major categories of potentially harmful chemicals: carbonyls, volatile organic chemicals, trace elements, reactive oxygen species and free radicals, polycyclic aromatic hydrocarbons, and tobacco-specific nitrosamines. In general, higher concentrations of aerosol toxicants are associated with increased power or voltage. Aerosol toxicants are also associated with e-liquid flavoring agents existing as primary ingredients or as products of thermal degradation. CONCLUSIONS: Improved ENDS design can reduce toxicant levels. Additional research is needed to develop a framework for optimizing system characteristics to minimize exposure, especially with respect to heating power and e-liquids. Both manufacturers and regulatory agencies have roles in reducing toxicants and potential health risks from ENDS.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".