Question · asked by 1 review
Electronic cigarette use vs Non-smokers: TNF-α level
- Population
- People who use electronic cigarettes
- Intervention
- Electronic cigarette use
- Comparison
- Non-smokers
- Outcome
- TNF-α level
The review reports no significant e-cigarette effect on salivary TNF-α.
What each review found
Include earlier review versionsNo studies found 0 · 0%GRADE not reported 3 · 100%Very low 0 · 0%Low 0 · 0%Moderate 0 · 0%High 0 · 0%
| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
Electronic cigarette use vs Non-smokers TNF-α level · People who use electronic cigarettes · Saliva, Saliva The review reports no significant e-cigarette effect on salivary TNF-α. The effects of electronic smoking on dental caries and proinflammatory markers: a systematic review and… (2025) All outcomes | Cohen's d 0.23 [-0.62, 1.09] | 5 studies | GRADE not reported |
Electronic cigarette use vs Non-smokers TNF-α level · People who use electronic cigarettes · Gingival crevicular fluid, GCF The review reports that GCF evidence was limited and may show a potential association. The effects of electronic smoking on dental caries and proinflammatory markers: a systematic review and… (2025) All outcomes | Cohen's d 2.19 [1.53, 2.84] | 2 studies | GRADE not reported |
Electronic cigarette use vs Non-smokers TNF-α level · People who use electronic cigarettes · Overall The review reports no e-cigarette effect on salivary TNF-α, while findings for GCF may differ. The effects of electronic smoking on dental caries and proinflammatory markers: a systematic review and… (2025) All outcomes | Cohen's d 0.66 [-0.16, 1.43] | 7 studies | GRADE not reported |
Findings are shown side by side, never pooled across reviews.
The reviews
| Review | Searched up to | Included studies | AMSTAR 2 |
|---|---|---|---|
| The effects of electronic smoking on dental caries and proinflammatory markers: a systematic review and meta-analysis. 2025 All outcomes | September 2024 (initial search April 2024, updated September 2024) | 41 | Not applicable |
How reviews are matched to this question
Reviews word the same question differently. Four decision models (TypeSafe's Jev, Liquid AI's d1-ultra, SubQ Snap and Baseten's Elephant) judged each wording of the population, intervention, comparator and outcome against the others; two wordings count as the same when at least three of them agree and none clearly disagrees, and findings count as this question only when all four elements match. Timing, setting and subgroup never split a question: they are shown on each finding.