Salivary Biomarkers in Denture Stomatitis: A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: Denture stomatitis (DS) is an oral biofilm-associated inflammation of the denture-bearing mucosa. The objective of this review was to identify and evaluate the quality of evidence on the association between the levels of salivary biomarkers and DS among adults with and without palatal DS. MATERIALS AND METHODS: Following the PRISMA guidelines, Medline, PubMed, EMBASE, and the Cochrane Central Register for Controlled Trials were searched for eligible studies from the beginning of the archives until December 2018. Experimental and observational studies with adult participants were included that had a control group or subgroup analysis and provided data on salivary biomarkers and DS. Articles in languages other than English or French were excluded. The level of evidence and grades of recommendation were established with the 2011 scale of the Oxford Centre for Evidence-Based Medicine. Additionally, the assessment of methodological quality was conducted with the STROBE statement (Strengthening the Reporting of Observational Studies in Epidemiology) and graded according to the Olmos scale. RESULTS: < 0.05). In contrast, 2 studies concluded that the difference in the levels of several salivary cytokines (IL2, IL12, IFN-g, IL-4, IL-8, IL-10, IL-17, TNF-α, and ICAM-1) between the groups was not statistically significant. The level of evidence for the majority of studies was 3, while the grade of recommendation for all the studies was B, interpreted as "favorable." In terms of methodological quality, most studies met 50% to 80% of STROBE criteria and were graded B. CONCLUSION: Palatal inflammation in DS is significantly associated with the levels of salivary cytokines. KNOWLEDGE TRANSFER STATEMENT: The results of this study identified altered levels of specific salivary biomarkers associated with denture stomatitis, which may aid in the early diagnosis and treatment of this disease.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".