Correlation between salivary nitric oxide concentration and dental caries prevalence in children – a systematic review and meta-analysis
Bibliographic record
Abstract
Background. As a potential biomarker for caries risk assessment, salivary nitric oxide (NO) levels and dental caries prevalence in children have been linked. This has attracted a lot of attention in dental research. Aim. To investigate the relationship between salivary nitric oxide concentration and the prevalence of dental caries in children aged 0-12 years. Methodology. A comprehensive search was conducted from 2016 to 2024 across PubMed, Scopus, Web of Science, and Cochrane Library databases, following PRISMA guidelines. Three reviewers screened 238 articles. Data on caries-related outcomes, nitric oxide measurement methods, and research characteristics were extracted. The Newcastle-Ottawa Scale (NOS) assessed the quality of case-control and cross-sectional studies, while the ROBINS-I tool was used for in vivo research quality evaluation. Results. This systematic review examined 9 studies on the relationship between salivary nitric oxide (NO) levels and early childhood caries (ECC). The research generally showed lower NO levels in ECC groups compared to controls, suggesting a potential preventive role of NO in caries development. Some studies found a correlation between higher NO levels and lower caries incidence. Meta-analysis confirmed a significant inverse relationship between salivary NO levels and caries prevalence, supporting its potential as a biomarker for evaluating caries risk in children and its application in preventive dental care. Conclusion. Salivary nitric oxide levels have the potential to be used as a biomarker for determining a child's caries risk because they are correlated with lower incidence of dental caries. Increasing research and standardizing procedures are essential to confirming NO's diagnostic value in everyday dentistry.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.031 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| 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".