Exploring potential salivary biomarkers for dental caries: a systematic review
Bibliographic record
Abstract
BACKGROUND: Dental caries remains one of the most widespread non-communicable diseases. Saliva is crucial for maintaining oral health as it shields teeth from demineralization and promotes the remineralization of enamel. Although ongoing studies are investigating the relationship between various salivary proteins and dental caries, consensus in existing literature has not yet been established. This study aims to provide additional insights into the current research of salivary protein biomarkers association with dental caries. MATERIAL AND METHODS: This systematic review analyzed literature published between January 2013 to December 2023, retrieved from PubMed, Scopus, and Web of Science. RESULTS: The review included 21 observational studies (2 cohort, 2 case-control, and 17 cross-sectional) involving over 2,000 participants, examining 18 different proteins. There was considerable variability in the types of salivary markers studied. Among the participants, 54% were diagnosed as caries-active (CA), while 45.9% were caries-free (CF), with ages ranging from 6 to 89 years. The Newcastle-Ottawa Scale indicated that the risk of bias was low in 10 studies, intermediate in 10, and high in 1. CONCLUSIONS: Eighteen studies found significant differences in protein expression between CA and CF subjects, underscoring the potential of using salivary biomarkers for non-invasive diagnose assessment. However, larger and greater designed studies are needed to establish their clinical value. Besides, divergent results from proteomic studies on biomarkers may be due to variations in genetics, diet, oral hygiene, age and other factors of the subjects, which could affect the reliability of saliva biomarkers in caries screening and detection. The significant heterogeneity among studies made conducting a proper meta-analysis infeasible.
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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 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".