Relationship Between Salivary Nitric Oxide Concentration and Dental Caries in Children: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Context: Conflicting results have been reported in the literature concerning the relationship between salivary nitrous oxide concentration and dental caries in children. Metaanalysis studies aim to combine different studies and reduce the difference between the parameters by increasing the number of studies involved in the analysis process. Objectives: Accordingly, this meta-analysis study aimed at determining the relationship between salivary nitrous oxide concentration and dental caries in children. Methods: Databases were searched using the keywords “nitric oxide”, “salivary”, “Caries”, “DMFT Index”, “children”, “early childhood caries” and OR, AND and NOT operators. Quality assessment was then performed based on the Newcastle-Ottawa scale (NOS) checklist. The standardized mean difference (SMD) of DMFT, dmft, and salivary nitric oxide (NO) concentration was estimated. Results: Seven studies made a comparison between the mean salivary NO concentration in children with dental caries and that in the control group. In four studies, the mean salivary NO concentration in children with dental caries was lower, as compared to that in the control group. This difference was significant in all four studies. Also, the mean standardized difference of the salivary NO index was also estimated to be -0.11 (CI 95%: -1.77, 1.55). Conclusions: This meta-analysis study demonstrated that salivary NO concentration was not significantly related to dental caries. Moreover, since salivary NO concentration is affected by various factors, it is not sufficient to determine the likelihood of the incidence of caries.
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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 | 0.019 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.031 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".