Salivary and serum biomarkers to evaluate psychological disorders in burning mouth syndrome: A systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND: Burning mouth syndrome is a chronic pain syndrome mainly characterized by an intensive burning sensation of tongue. Previous studies have suggested that saliva/serum biomarkers in burning mouth syndrome might be associated with psychological disorders. The aim of systematic review was to observe whether the biomarkers in serum/saliva could be an alternative method to evaluate the psychological disorders in patients with burning mouth syndrome. MATERIALS AND METHODS: The PubMed, Embase, and Cochrane Library databases were searched for papers published up to March 15, 2023. Risk of bias was measured by using the Newcastle-Ottawa Scale. RevMan was used for meta-analysis. RESULTS: A total of 467 articles were screened, which of 12 studies were included. These studies collected 43 different biomarkers in saliva and 35 in serum. Of these biomarkers, only three (cortisol, α-amylase, and IL-6) were analyzed in two or more studies. Only salivary cortisol levels were significantly higher in the patient group compared to the controls (Mean Difference = 1.39; 95% CI [0.80-1.97]; p < 0.001). Moreover, cortisol might be relevant to psychological scores, especially anxiety. CONCLUSION: Different papers have investigated salivary and serum biomarkers in burning mouth syndrome patients with controversial results. This meta-analysis showed that cortisol levels in saliva may be a potential biomarker to assess the psychological disorders in burning mouth syndrome patients.
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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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.030 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".