Schematic Assessment of Different Mouthrinses (Chlorhexidine, Herbal, Fluoride) on Oral Microbiota Composition: A Meta-Analysis Perspective
Bibliographic record
Abstract
Background: The oral microbiota helps keep your mouth and body healthy, and the regular use of mouthrinses can change its makeup. The purpose of this study was to evaluate and compare the ways that chlorhexidine, herbal, and fluoride-containing mouthrinses change the makeup and variety of oral microbiota. Methods: A search was carried out on PubMed, Scopus, Web of Science, and Google Scholar for studies published between 2014 and 2024. Only studies that tested chlorhexidine, herbal, or fluoride mouthrinses on human participants by means of randomized trials or observational studies were included. Changes in microbial diversity (such as α-diversity) and the proportions of major taxa were the primary outcomes studied. The quality of the studies was assessed using both the Cochrane Risk of Bias tool and the Newcastle-Ottawa Scale. Results: Twelve studies were selected for inclusion in the review according to the criteria. The pooled SMD value was −1.32 (95% CI: −2.80 to 0.17; p = 0.083), which suggests there is no statistically significant effect of experimental mouthrinses on microbial levels. The results showed great variability (I² = 96.4%; p < 0.0001), possibly because of the variety in study design, type of mouth rinse, and methods used to assess microbiota. Sensitivity analysis found that the results were not heavily swayed by any one study. Even though six studies observed a decrease in microbes, mainly as a result of chlorhexidine. Discussion: There are differences in how mouthrinses influence oral bacteria. Although chlorhexidine can help reduce pathogenic species, it is linked to microbial dysbiosis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".