Vaping and Early Periodontal Damage in Teens: Associations with Community Periodontal Index Scores and Salivary Inflammatory Markers
Bibliographic record
Abstract
Electronic cigarette (e-cigarette) use has grown dramatically among adolescents, and there has been growing concern about the possible impact on oral and periodontal health. Although the perceived risks of vaping are less than conventional smoking, e-cigarette aerosols contain nicotine, aldehydes, and metal particulates which have the potential to change the oral microenvironment and inflammatory pathways. This study set out to find if e-cigarette use is associated with sub-clinical periodontal changes in high-school students by using Community Periodontal Index (CPI) scores and salivary inflammatory biomarkers as indicators of periodontal changes. A cross-sectional analytical design was used among adolescents aged 14-18 years old adapted into vapers and non-vapers. Clinical oral evaluations were performed by CPI, and the saliva samples were examined for interleukin (IL)-1v, IL-6 and tumor necrosis factor-alpha (TNF-a) levels using enzyme-linked immunosorbent assay (ELISA). Preliminary findings from similar studies have shown higher CPI scores with significantly higher salivary cytokine concentrations in adolescent e-cigarette users than in their non vaping counterparts suggesting the development of early periodontal inflammation before overt clinical disease. These results emphasize vaping as a possible risk factor for early changes in the periodontal tissues of the adolescent and recommend early detection, public health education and preventive measures in school-based oral health programs.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".