e-Cigarette Use and the Cessation of Tobacco Cigarette Smoking: Protocol for an Umbrella Review
Bibliographic record
Abstract
BACKGROUND: Electronic nicotine delivery systems (ENDS), commonly called e-cigarettes, have been examined in clinical studies for their effects on tobacco smoking cessation. In the past 2 years, a dozen or more systematic reviews on ENDS and cigarette smoking cessation have been published that present differing conclusions and recommendations on the use of ENDS. OBJECTIVE: Our umbrella review aims to synthesize the findings from current systematic reviews to investigate the quit rates and the percentage of participants abstinent from cigarette smoking using ENDS. Additionally, we will examine the quit rates with ENDS in comparison to other established cessation treatments. METHODS: The search will retrieve systematic reviews that include both clinical trials and experimental studies on the use of ENDS for smoking cessation. We will also include nonrandomized cohort studies that track ENDS use and the subsequent abstinence from smoking. Databases searches will be conducted in Embase, Scopus, PubMed, and 7 additional registries. Secondary searches will include reference checking, citation chasing, and consultations with topic experts. Two reviewers will perform a title and abstract exclusion followed by a full-paper inclusion process. Data extraction will be conducted by 1 reviewer and completely checked by a second reviewer. Each systematic review will be assessed by 2 reviewers for methodological quality using AMSTAR2 (A Measurement Tool to Assess Systematic Reviews, version 2) and for reporting bias using categories from the Centre for Evidence-Based Medicine's Catalogue of Bias. Unreported discrepancies between the protocol and the published review will be identified. RESULTS: The umbrella review started on March 1, 2023. At the time of publication, the study selection was being conducted and the pilot testing of the data extraction and bias assessment forms were in progress. The review is expected to be completed by December 31, 2023, followed by the submission of the review for journal publication. A second-order meta-analysis will calculate the range and average of quit rates for ENDS. A vote counting of the direction of effect, based on quit rates, will be used to present the relative effectiveness of ENDS for smoking cessation compared to other cessation treatments (including no treatment). A citation matrix will list primary studies with their bias ratings from all the systematic reviews. The effect of overlapping studies between the systematic reviews will be calculated using the corrected coverage area analysis. A sensitivity analysis will examine the impact of the intensity of cessation treatment on quit rates. Depending on the availability of data, subgroup analyses will be conducted based on participants' gender, age, prior quit attempts, and nicotine dependence. The strength or weakness of the evidence synthesis will be assessed using a stratification of evidence technique. Reporting bias will be presented with a tabulation of bias indicators. Publication bias will be assessed. CONCLUSIONS: The use of ENDS for smoking cessation is a highly controversial subject. Through an exhaustive synthesis of the available data, we will present the quit rates of cigarette smoking cessation obtained with ENDS and how they compare to quit rates obtained from other established cessation treatments. The critical quality and bias assessment of the systematic reviews will indicate the most reliable sources to inform treatment considerations and policy development. TRIAL REGISTRATION: PROSPERO CRD42023406165; https://tinyurl.com/4ekzpbrj. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/47711.
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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 | 0.076 | 0.090 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.012 | 0.015 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.105 | 0.023 |
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".