The health effects of vaping and e-cigarettes: consensus recommendations
Bibliographic record
Abstract
OBJECTIVE: To develop evidence-informed recommendations on the health effects of e-cigarettes to guide healthcare practitioners and the public to balance individual and population harm reduction. METHODS: Systematic and umbrella reviews investigating the health effects of e-cigarette use were conducted (September 2017 - January 2024). An international panel of subject matter experts (n = 23) and people in Canada with lived experience (n = 7) participated in a two-day, hybrid meeting, and used a consensus-based approach to develop recommendations. A guidance resource and four accompanying knowledge products were tested for usability with end users. RESULTS: Consensus was reached on 14 recommendations spanning four health effects: carcinogen exposure, cardiovascular health, respiratory health, and nicotine dependence. Quality of evidence was voted as ranging from high/moderate to moderate/low, and strength of most recommendations was voted as strong. CONCLUSIONS: Guidance has been informed by best available evidence and expertise, providing direction to support decision-making. The use of established methods to evaluate divergent published literature combined with consensus-building methods among a range of stakeholders on vaping is possible. As higher quality evidence continues to emerge, recommendations will require iterative refinement.
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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 | 0.098 | 0.226 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.009 |
| Bibliometrics | 0.014 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.009 | 0.008 |
| Research integrity | 0.014 | 0.015 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".