Electronic Nicotine Delivery Systems (“E‐cigarettes”): Review of Safety and Smoking Cessation Efficacy
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
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.
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) | 0.003 | 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 | 0.001 | 0.001 |
| 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