Matrix metalloproteinases and myeloperoxidase in gingival crevicular fluid provide site‐specific diagnostic value for chronic periodontitis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 it