Clinical peri‐implant parameters and inflammatory cytokine profile among smokers of cigarette, e‐cigarette, and waterpipe
Bibliographic record
Abstract
BACKGROUND: To date, there has been no study identified that assessed peri-implant parameters and local levels of proinflammatory cytokines between waterpipe smokers (WS) and electronic cigarette users (VS). To evaluate the true effect of the risk indicator (WS and VS), a positive control (cigarette smokers [CS]) was added in the study design. PURPOSE: To compare clinical and radiographic peri-implant parameters and proinflammatory cytokine profile in the peri-implant sulcular fluid (PISF) among CS, WS, VS, and nonsmokers (NS). MATERIALS AND METHODS: Forty CS, WS, VS, and NS were included. Details regarding demographics, duration of implants, and habits were recorded. Clinical examination for periodontal and peri-implant plaque index (PI), bleeding on probing (BOP), and probing depth (PD ≥ 4 mm) were assessed. Radiographic bone loss (RBL) was assessed using digital periapical radiographs and viewed on a calibrated computer screen using an image analyzer. Levels of tumor necrosis factor-alpha (TNF-α), interleukin (IL)-6, and IL-1β in PISF were evaluated using enzyme-linked immunosorbent assay. RESULTS: Mean peri-implant PI (P < .05), PD ≥ 4 mm (P < .05), and total RBL (P < .01) was significantly higher among CS, WS, and VS compared with NS. Statistical differences in BOP were observed in NS (P < .01) compared to CS, WS, and VS. CS and WS showed significantly higher PD ≥ 4 mm and RBL compared with VS (P < .05). Levels of TNF-α, IL-6, and IL-1β were significantly higher in CS, WS, and VS compared to NS. There were no statistical differences in the mean levels of all proinflammatory cytokines among individuals in CS and WS. CONCLUSION: Tobacco smoking is associated with poor peri-implant health. The effect of waterpipe smoking was of largely the same extent as that of cigarette smoking. Higher levels of inflammatory cytokines in tobacco smokers may propose increased peri-implant inflammatory process which could play a principal role in the progression of peri-implant tissue damage.
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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 | 0.000 | 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".