Electronic Nicotine Delivery Systems (“E‐cigarettes”): Review of Safety and Smoking Cessation Efficacy
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Cigarette smoking is common among cancer patients and is associated with negative outcomes. Electronic nicotine delivery systems ("e-cigarettes") are rapidly growing in popularity and use, but there is limited information on their safety or effectiveness in helping individuals quit smoking. DATA SOURCES: The authors searched PubMed, Web of Science, and additional sources for published empirical data on safety and use of electronic cigarettes as an aid to quit smoking. REVIEW METHODS: We conducted a structured search of the current literature up to and including November 2013. RESULTS: E-cigarettes currently vary widely in their contents and are sometimes inconsistent with labeling. Compared to tobacco cigarettes, available evidence suggests that e-cigarettes are often substantially lower in toxic content, cytotoxicity, associated adverse effects, and secondhand toxicity exposure. Data on the use of e-cigarettes for quitting smoking are suggestive but ultimately inconclusive. CONCLUSIONS: Clinicians are advised to be aware that the use of e-cigarettes, especially among cigarette smokers, is growing rapidly. These devices are unregulated, of unknown safety, and of uncertain benefit in quitting smoking. IMPLICATIONS FOR PRACTICE: In the absence of further data or regulation, oncologists are advised to discuss the known and unknown safety and efficacy information on e-cigarettes with interested patients and to encourage patients to first try FDA-approved pharmacotherapies for smoking cessation.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".