Matrix metalloproteinases and myeloperoxidase in gingival crevicular fluid provide site‐specific diagnostic value for chronic periodontitis
Bibliographic record
Abstract
AIM: To identify the diagnostic accuracy of gingival crevicular fluid (GCF) candidate biomarkers to discriminate periodontitis from the inflamed and healthy sites, and to compare the performance of two independent matrix metalloproteinase (MMP)-8 immunoassays. MATERIALS AND METHODS: Cross sectional study. GCF (N = 58 sites) was collected from healthy, gingivitis and chronic periodontitis volunteers and analysed for levels of azurocidin, chemokine ligand 5, MPO, TIMP-1 MMP-13 and MMP-14 by ELISA or activity assays. MMP-8 was assayed by immunofluorometric assay (IFMA) and ELISA. Statistical analysis was performed using linear mixed-effects models and Bayesian statistics in R and Stata V11. RESULTS: MMP-8, MPO, azurocidin and total MMP-13 and MMP-14 were higher in periodontitis compared to gingivitis and healthy sites (p < 0.05). A very high correlation between MPO and MMP-8 was evident in the periodontitis group (r = 0.95, p < 0.0001). MPO, azurocidin and total levels of MMP-8, MMP-13 and MMP-14 showed high diagnostic accuracy (≥0.90), but only MMP-8 and MPO were significantly higher in the periodontitis versus gingivitis sites. MMP-8 determined by IFMA correlated more strongly with periodontal status and showed higher diagnostic accuracy than ELISA. CONCLUSIONS: MPO and collagenolytic MMPs are highly discriminatory biomarkers for site-specific diagnosis of periodontitis. The comparison of two quantitative MMP-8 methods demonstrated IFMA to be more accurate than ELISA.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".