Host‐derived salivary biomarkers in diagnosing periodontal disease: systematic review and meta‐analysis
Bibliographic record
Abstract
AIM: To systematically evaluate the accuracy of host-derived salivary biomarkers in the diagnosis of periodontal disease based on the given sensitivity and specificity information. MATERIALS AND METHODS: Studies were eligible for inclusion if they had compared the diagnostic application of salivary biomarkers with clinical examination of periodontal disease. A detailed search was performed in five databases without restrictions on subject age, chronology, or language. Additionally, a partial grey-literature search was conducted. The revised Quality Assessment of Diagnostic Accuracy Studies tool and Meta-analysis were used to evaluate the selected studies. RESULTS: From the 905 screened studies, four were included in the qualitative and quantitative analysis. One biomarker, macrophage inflammatory protein-1α (MIP-1α), had excellent diagnostic accuracy and two, interleukin-1β (IL-1β) and interleukin-6 (IL-6), showed acceptable diagnostic values. However, the only biomarker considered excellent was evaluated in a single study, which may reduce the robustness of the results. CONCLUSION: There is currently limited evidence to confirm the diagnostic capability of salivary biomarkers in the clinical assessment of periodontal disease. Notwithstanding, the summary findings showed the growing importance of salivary biomarker, and can guide larger, well-controlled, diagnostic accuracy studies. Likewise, although not conclusive, MIP-1α, IL-1β, and IL-6 may be promising biomarkers for future studies.
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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 | 0.013 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.018 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".