Impact of Cigarette Smoking on Peri-implant Cytokine Profiles: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Abstract Studies have suggested that cigarette smoking may increase inflammation around dental implants by inducing higher levels of proinflammatory cytokines in diseased and healthy implants. The purpose of this article is to systematically compare peri-implant cytokine profiles around healthy and diseased implants in smokers versus non-smokers. Using appropriate MeSH and keywords, an online search was conducted for prospective clinical studies in which the peri-implant cytokines in healthy and diseased dental implants in smokers were compared with those in non-smokers. The data and outcomes were tabulated, and the quality of the literature was assessed. Meta-analysis was conducted on cytokines that had been evaluated in more than one study. Of the 1,592 items, 10 articles were included in this review. In heathy dental implants, cigarette smoking had a statistically insignificant effect on the cytokine profile. During peri-implantitis, smoking had a more significant effect on the levels of proinflammatory cytokines such as IL-1β and MMP-9, but there was significant heterogeneity between the studies and the sample size was not adequate to ascertain the overall long-term impact. Nine out of 10 studies in this review had several sources of bias and were of low quality, and one study had a moderate quality. Within the limits of this systematic review, it may be suggested that cigarette smoking aggravates peri-implantitis by influencing the cytokine profile to proinflammatory. The effect of cigarette smoking on clinically healthy implants is uncertain.
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.020 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".