Electronic Cigarettes in North America
Bibliographic record
Abstract
BACKGROUND: Designed to mimic the look and feel of tobacco cigarettes, electronic cigarettes (e-cigarettes) may facilitate smoking cessation. However, the efficacy and safety of e-cigarette use for this purpose remain poorly understood. Our objectives were to review the available data on the efficacy and safety of e-cigarettes for smoking cessation and to consider issues relevant to the context in which they are used, including product awareness and regulatory and ethical concerns. METHODS AND RESULTS: We systematically searched PubMed for randomized controlled trials and uncontrolled, experimental studies involving e-cigarettes. Included studies were limited to English or French language reports. Quality assessment was performed according to the Cochrane Risk of Bias tool. We identified 169 publications, of which 7 studies were included. Studies have concluded that e-cigarettes can help reduce the number of cigarettes smoked and may be as effective for smoking cessation as the nicotine patch. Although there is a lack of data concerning the safety and efficacy of e-cigarettes as a smoking cessation therapy, available evidence showed no significant difference in adverse event rates between e-cigarettes and the nicotine patch. E-cigarettes are widely used among smokers attempting to quit. However, significant international variation remains in the regulatory mechanisms governing the sale and distribution of e-cigarettes. Ethical concerns surround the use of e-cigarettes among minors and their potential to undermine efforts to reduce cigarette smoking. CONCLUSION: Given the limited available evidence on the risks and benefits of e-cigarette use, large, randomized, controlled trials are urgently needed to definitively establish their potential for smoking cessation.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".