Use of IL‐1 β, IL‐6, TNF‐α, and MMP‐8 biomarkers to distinguish peri‐implant diseases: A systematic review and meta‐analysis
Bibliographic record
Abstract
OBJECTIVE: To investigate the use of peri-implant crevicular fluid (PICF) interleukin-1β (IL-1β), IL-6, tumor necrosis factor-α (TNF-α), and matrix metalloproteinase-8 (MMP-8) biomarkers in distinguishing between healthy implants (H), peri-implant mucositis (MU), and peri-implantitis (PI). MATERIAL AND METHODS: Electronic using three databases (Pubmed, EMBASE, and Cochrane) and manual searches were conducted for articles published up to March 2018 by two independent calibrated reviewers. Meta-analyses using a random-effects model were conducted for each of the cytokines; IL-1β, IL-6, and TNF-α, to analyze standardized mean difference (SMD) between H and MU, MU and PI, H and PI with their associated 95% confidence intervals (CI). Qualitative assessment of MMP-8 was provided consequent to the lack of studies that provide valid data for a meta-analysis. RESULTS: Nineteen articles were included in this review. IL-1β, IL-6, and TNF-α, levels were significantly higher in MU than H groups (SMD: 1.94; 95% CI: 0.87, 3.35; P < .001, SMD: 1.17; 95% CI: 0.16, 3.19; P = .031 and SMD: 3.91; 95% CI: 1.13, 6.70; P = .006, respectively). Similar results were obtained with PI compared to H sites (SMD: 2.21, 95% CI: 1.32, 3.11; P < .001, SMD: 1.72; 95% CI: 0.56, 2.87; P = .004 and SMD: 3.78; 95% CI: 1.67, 5.89; P < .001, respectively). IL-6 was statistically higher in PI than MU sites (SMD = 1.46; 95% CI: 0.36, 2.55; P = .009); while IL-1ß increase was not significant. Despite absence of meta-analysis, MMP-8 show to be a promising biomarker in detection of PI in literature. CONCLUSION: Within the limitations of this study, pro-inflammatory cytokines in PICF, such as IL-1ß and IL-6, can be used as adjunct tools to clinical parameters to differentiate H from MU and PI.
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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 | 0.015 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.033 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".