Salivary matrix metalloproteinase (MMP)-8 as a biomarker for periodontitis
Bibliographic record
Abstract
BACKGROUND: Salivary matrix metalloproteinase (MMP)-8 is currently considered to be one of the most promising biomarkers for early diagnosis of periodontitis, however, several recent studies showed conflicting results. OBJECTIVE: To determine the salivary matrix metalloproteinase (MMP)-8 levels between periodontitis patients and healthy individuals, and to assess its diagnostic value in periodontitis. METHODS: Literatures were searched on PubMed and Embase databases up to August 2017, for articles reporting salivary MMP-8 levels between periodontitis patients and health controls with the data of means ± standard deviation (SD). Methodological quality was assessed by the Newcastle Ottawa scale (NOS). Standard mean differences (SMDs), heterogeneity, and publication bias were assessed by Stata 13.0 software. RESULTS: A total of 10 studies including 485 periodontitis patients and 379 healthy controls that met the preset inclusion criteria were included, the qualities of these studies were either good (n = 7) or moderate (n = 3). Eight studies showed salivary MMP-8 levels were higher in periodontitis patients compared with healthy controls (P < .05), while 2 studies showed opposite results (P > .05). The pooled SMD was 1.195 (95% CI: 0.720-1.670), with I of 89.3%, indicating high heterogeneity. Funnel plot showed publication bias existed. CONCLUSION: Our meta-analysis showed that salivary MMP-8 levels were significantly higher in periodontitis patients compared with healthy controls overall. Due to the heterogeneity and publication bias of included studies, further high quality studies are still needed to verify the conclusion.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".