Salivary biomarkers in burning mouth syndrome: A systematic review and meta‐analysis
Bibliographic record
Abstract
The objective of this systematic review was to evaluate which salivary biomarkers are altered in patients with burning mouth syndrome (BMS) compared to a control group (CG). A comprehensive literature search was conducted in four databases. Case-control studies evaluating salivary biomarkers in BMS patients were included. Risk of bias was assessed using the Newcastle-Ottawa tool. RevMan was used for meta-analysis. Seventeen studies were selected. The included studies collected 54 different biomarkers. Of these biomarkers, only three (cortisol, α-amylase, and dehydroepiandrosterone) were analyzed in three or more studies. Dehydroepiandrosterone obtained contradictory results among the studies. However, cortisol and α-amylase levels were found to be higher in BMS patients. Cortisol was the only biomarker which could be included for meta-analysis. Cortisol levels were significantly higher in the BMS group compared to the CG (Mean Difference = 0.39; 95% CI [0.14-0.65]; p = 0.003). In conclusion, different studies investigated salivary biomarkers in patients with BMS compared to a CG, with controversial results. Meta-analysis, confirmed by trial-sequential analysis, showed how cortisol levels were significantly higher in BMS. Cortisol emerges as an interesting salivary biomarker in BMS, but future properly designed studies are needed to evaluate its role in diagnosis and/or response to treatment.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.019 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".