Impact of Smoking on Macrophage‐Related Chemokines During Initial Peri‐Implantitis: A Prospective Cohort Study
Bibliographic record
Abstract
OBJECTIVES: Smoking disrupts macrophage chemokine response and delays healing. This study aims to investigate the effect of smoking on peri-implant crevicular fluid (PICF) levels of macrophage-related chemokines, C-C motif chemokine ligand 2 (CCL-2), C-C motif chemokine ligand 8 (CCL-8), C-X-C motif chemokine ligand 9 (CXCL-9), and C-C motif ligand 3 (CCL-3), before and after non-surgical treatment of initial peri-implantitis. METHODS: Fifty-five implants (27 non-smoking [NSPI] and 28 smoking [SPI]) with initial peri-implantitis (bleeding on probing [BOP+], probing pocket depth [PPD] of 6-7 mm) were included in the study. Clinical parameters were recorded, and PICF samples were collected before and 4 months after non-surgical treatment. PICF concentrations of CCL-2, CCL-8, CCL-3, and CXCL-9 were measured with Luminex assay. The Mann-Whitney U-test, Wilcoxon signed-rank test, and repeated measures analysis of variance test were used to analyze differences between and within the groups. RESULTS: Baseline CCL-2 (p < 0.001) and CXCL-9 (p = 0.026) levels (pg/30 s) were significantly lower in smokers compared to non-smokers, while no difference was observed for CCL-3 between the two groups (p = 0.320). Only CCL-2 levels (pg/30 s) decreased in the NSPI group in response to non-surgical treatment (p = 0.037). CONCLUSION: Smoking disturbs the expressions of macrophage-related chemokines in the early phase of peri-implantitis. These findings may indicate the impaired control of infection during initial peri-implantitis and explain the accelerated progression of the disease in smokers. This study was not registered prior to participant recruitment. TRIAL REGISTRATION: https://clinicaltrials.gov/study/NCT06810401.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".