What should every dental health professional know about electronic cigarettes?
Bibliographic record
Abstract
Electronic cigarettes (E-cigarettes) have become increasingly popular around the world. Currently, dental professionals' knowledge and attitudes are varied with many clinicians unclear regarding the impact of E-cigarette products on the oral and general health of their patients. With developing social and health-related challenges, advice of dental and medical associations and other regulatory bodies on E-cigarette use is changing. Growing evidence demonstrating the risks of E-cigarette usage has prompted a review of legislation in the United Kingdom (UK), United States of America (USA), Australia and Canada to include the sale and availability of E-cigarettes, particularly those containing nicotine. Further consideration within the scientific and public health community is being given to assessing demographic usage patterns particularly uptake by non-smokers and adolescents, efficacy as a cessation tool, the impact of vapour on bystanders and direct injuries via explosions as well as emerging lung injuries. This article aims to provide a summary of the most up to date evidence relating to E-cigarette use, the latest position of dental associations and the oral health implications of E-cigarettes compared to conventional smoking. The article also aims to collate this information in order to provide dental clinicians with guidance on how to advise patients, specifically in answering common questions posed regarding E-cigarette use.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".