A Systematic Review Investigating Associations Between E-Cigarette Use Among Former Cigarette Smokers and Relapse to Smoking Cigarettes
Bibliographic record
Abstract
As e-cigarette use has steadily increased over the recent years, the public health interest in the potential implications of e-cigarette use on cigarette smoking has grown in parallel. With strict adherence to PRISMA guidelines, this systematic review examined the potential associations between e-cigarette use and relapse to cigarette smoking among former cigarette smokers. The protocol was registered on November 06, 2018 (PROSPERO 2018 CRD42018115674). Literature searches were executed from January 01, 2007 to August 20, 2022 and search results were screened according to the PICOS review method. One RCT and 10 adjusted studies examined relapse to cigarette smoking (evidence grade “moderate”) among regular e-cigarette users, reporting mixed and inconsistent findings according to varying definitions of e-cigarette use and relapse. Findings were similarly inconsistent among the 8 adjusted studies examining relapse to cigarette smoking among non-regular e-cigarette users. The inconsistency in findings among studies evaluating regular measures of e-cigarette use, combined with the numerous methodological flaws in the overall body of literature, limit the generalizability of results associated with a causal association between e-cigarette use and relapse to cigarette smoking. Based on findings from this review, more robust studies are required to determine whether a causal association exists between e-cigarette use and relapse to cigarette smoking. Future studies should apply consistent measures of regular e-cigarette use to examine causality with future use patterns, and sufficiently account for known or suspected confounding variables to support inform determinations related to e-cigarette use and cigarette smoking behaviors.
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.
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 | 0.010 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".