Evidence update on e-cigarette dependence: A systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: We conducted this review to examine the risk of e-cigarette dependence in different populations by updating the review on this topic by the National Academies of Science, Engineering, and Medicine. METHODS: Six academic databases were searched for studies published between September 2017 and December 2023. We included peer-reviewed human, animal, cell/in vitro original studies examining associations of e-cigarette use and dependence but excluded qualitative studies. Three types of e-cigarette exposure were examined: acute, short-to-medium term, and long-term. Meta-analysis were conducted when possible. Different risk of bias tools were used for assessing quality of the included human studies. RESULTS: We included 107 studies, of which 34 studies were included in the subgroup analysis. Meta-analyses showed that non-smoker current vapers had no statistically significant difference in level or prevalence of dependence compared to non-vaper current smokers and dual users. However, never smoker current vapers had a lower level of dependence (SMD -0.723, p < 0.01) compared to dual users, which was also supported by ANOVA test. Narrative review findings suggest that nicotine vapers had higher level of dependence than non-nicotine vapers and e-cigarette dependence is positively associated with nicotine concentration, frequency, and duration of use. No strong relationship was found between dependence and product types or features. Socio-demographic factor-based subgroup findings were inconclusive. CONCLUSIONS: The level and prevalence of e-cigarette dependence is similar to cigarette dependence. There was high variability in the definitions and methods used for defining populations and assessing dependence. Further research and monitoring are crucial.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".