Comparison of cotinine levels in the peri‐implant sulcular fluid among cigarette and waterpipe smokers, electronic‐cigarette users, and nonsmokers
Bibliographic record
Abstract
BACKGROUND: Assessment of cotinine levels in the peri-implant sulcular fluid (PISF) may serve as a valuable biomarker of peri-implant diseases in nicotine-product users. PURPOSE: The aim of the present study was to compare cotinine levels in the PISF among cigarette smokers, waterpipe users, electronic-cigarette users, and nonsmokers. MATERIALS AND METHODS: Cigarette smokers, waterpipe smokers, electronic-cigarette users, and nonsmokers were included. A questionnaire was used to collect information about age, gender, duration of smoking and vaping, family history of smoking, duration of smoking/vaping, and daily frequency of smoking/vaping. Implant-related data including implant dimensions and duration of implants in function were also recorded. In all groups, peri-implant probing depth (PD), bleeding on probing (BoP), and plaque index (PI) were assessed. Using standard techniques, PISF was collected and levels of cotinine in the PISF were measured. Sample-size estimation was performed, and statistical comparisons were done using one-way analysis of variance and Bonferroni post hoc adjustment tests. P values below .05 were categorized as statistically significant. RESULTS: One hundred two male individuals (35 cigarette smokers, 33 waterpipe smokers, 34 electronic-cigarette users, and 35 nonsmokers) were included. Scores of peri-implant PI (P < .05) and PD (P < .05) were significantly higher among cigarette smokers, waterpipe smokers, and electronic-cigarette users compared with nonsmokers. Peri-implant BoP was more often manifested in nonsmokers compared with cigarette smokers (P < .05), waterpipe smokers (P < .05), and electronic-cigarette users (P < .05). The volume of collected PISF was significantly higher among cigarette (P < .05) and waterpipe smokers (P < .05) and electronic-cigarette users (P < .05) than nonsmokers. Cotinine levels were significantly higher in the PISF of cigarette (P < .05) and waterpipe smokers (P < .05) and electronic-cigarette users (P < .05) than nonsmokers. CONCLUSIONS: Habitual use of nicotinic products enhances the expression of cotinine in the PISF. Cotinine levels in the PISF of cigarette and waterpipe smokers and electronic-cigarette users are comparable.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".