Unintended Side Effects of Electronic Cigarettes in Otolaryngology: A Scoping Review
Bibliographic record
Abstract
OBJECTIVE: Electronic cigarettes (E-cigs) are nicotine delivery systems with increasing popularity. The US Food and Drug Administration defines side effects as unwanted or unexpected events or reactions. Our objective was to examine the unintended otolaryngology-related side effects associated with E-cigs. DATA SOURCES: Medline, EMBASE, CINAHL, Web of Science, and CENTRAL databases. REVIEW METHODS: Study selection was independently performed by 2 authors in accordance with the PRISMA-ScR statement (Preferred Reporting Items for Systematic Reviews and Meta-analyses Extension for Scoping Reviews); discrepancies were resolved by the senior author. English studies from database inception to May 1, 2020, with a sample size >5 were included. In vitro, animal, and lower respiratory tract studies were excluded. The main outcome was defined as otolaryngology-related side effects following E-cig use. Levels of evidence per the Oxford Centre for Evidence-Based Medicine were used to determine study quality. RESULTS: From 1788 articles, 32 studies were included. The most common unintended side effects were throat irritation (n = 16), cough (n = 16), mouth irritation (n = 11), and oral mucosal lesions (n = 8). A large proportion of participants also reported conventional tobacco use in addition to E-cigs. Eight studies investigated the effectiveness of vaping on smoking cessation. The quality of the literature was level 2 to 4. Given the significant heterogeneity in the studies, meta-analysis was not performed. CONCLUSION: The most reported side effects were throat and mouth irritation, followed by cough. The long-term impact of E-cigs is not known given the recent emergence of this technology. Future studies are warranted.
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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 | 0.012 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".