Salivary Inflammatory Mediator Profiles in Periodontal and Peri‐Implant Health and Disease: A Cross‐Sectional Study
Bibliographic record
Abstract
OBJECTIVE: This cross-sectional study aimed to investigate the salivary profile of inflammatory mediators in individuals with periodontal and peri-implant disease as compared to individuals with periodontal and peri-implant health. MATERIALS AND METHODS: Saliva samples were collected from 155 participants (mean age 63.3 ± 11.4 years), comprising individuals with periodontal and peri-implant health (N = 41), gingivitis and/or mucositis (N = 18), and periodontitis and/or peri-implantitis (N = 96). Samples were analyzed using multiplex-immunoassay panel consisting of inflammatory mediators in the tumor necrosis factor (TNF), interferon (IFN), interleukin (IL) superfamily, and matrix metalloproteinases. RESULTS: The levels of B-cell activating factor (BAFF), sIL-6Rβ, IFN-β, and sIL-6Rα, sTNFR1, and Pentraxin-3 were significantly higher in patients with periodontitis and/or peri-implantitis compared to healthy subjects. Furthermore, among the investigated inflammatory mediators, Pentraxin-3 exhibited the highest diagnostic potential (AUC = 0.74) for distinguishing between subjects with periodontitis and/or peri-implantitis and healthy individuals. CONCLUSIONS: Our findings demonstrated elevated salivary levels of BAFF, sIL-6Rβ, IFN-β, sIL-6Rα, sTNF-R1, and Pentraxin-3 in individuals with periodontitis and/or peri-implantitis in comparison to periodontal and peri-implant healthy controls.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".