Effect of Xylitol on Salivary Streptococcus Mutans: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Objectives: This study aimed to systematically review the available randomized clinical trials (RCTs) on the effect of xylitol on the number of Streptococcus mutans (S. mutans) colonies. Methods: An electronic search was carried out in Medline and Scopus databases for the RCTs published during 2002-2014. The inclusion criteria were evaluation of xylitol gums, having at least one control group and counting S. mutans colonies. The articles were divided into three groups based on the subjects’ age group namely 0-6, 6-18 and over 18 years. To assess the quality of RCTs, the retrieved articles were independently reviewed by two reviewers in terms of randomization and in order to prevent the effect of blinding on the results. Review Manager (RevMan) software, heterogeneity test and I2 coefficient as the quantitative scale of heterogeneity were used for statistical analysis. Results: Primary search of the literature using keywords related to sugar alcohols. After applying the inclusion criteria, 46 articles were found in PubMed and 356 in Scopus. Heterogeneity was not found in the two age groups of 6-18 and over 18 years and the I2 coefficient in these two groups was 0%. This rate for the 0-6 year-olds was 51% (P=0.15); which indicates moderate heterogeneity. The P value was 0.25, 0.34 and 0.04 for the 6-18, 0-6 and over 18 year-olds, respectively. This value was only significant for the over 18 year-old group. Data for all groups were analyzed irrespective of age, which revealed significant differences (P=0.01). Conclusions: The available literatures show xylitol as an alternative sweetener, could help to prevent dental caries by reducing the count of S. mutans in the saliva.
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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 | 0.017 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.031 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".