Efficacy of aloe vera mouthwash versus chlorhexidine on plaque and gingivitis: A systematic review
Bibliographic record
Abstract
OBJECTIVES: The present systematic review assessed the efficacy of aloe vera mouthrinse on plaque and gingival inflammation. METHODS: A comprehensive search of PubMed, EMBASE, Scopus and Web of Science was conducted in February 2018 to identify all relevant studies using the following keywords: aloe vera, gingivitis, gingival inflammation, plaque-induced gingivitis, periodontal health and plaque control. The eligibility criteria were all randomized clinical trials that assessed the efficacy of aloe vera mouthrinse in comparison to chlorhexidine on plaque and gingivitis. The risk of bias of the included studies was assessed using the Cochrane risk of bias assessment tool. RESULTS: Six randomized clinical trials comprising 1358 subjects were included in this systematic review. All included studies showed that aloe vera was effective in reducing plaque and gingival inflammation. Four studies found aloe vera as effective as chlorhexidine in reducing plaque scores, while two studies found chlorhexidine significantly more effective than aloe vera. With regard to gingival inflammation, three studies showed comparable results between aloe vera and chlorhexidine, while one study showed better results with chlorhexidine. Moreover, the results showed that aloe vera had no or very minimal side effects compared to chlorhexidine, which showed significant side effects including stains and altered taste sensation. CONCLUSION: The available evidence remains inconclusive but suggests that aloe vera mouthwash is comparable to chlorhexidine in reducing gingival inflammation but inferior to chlorhexidine in reducing plaque. These findings are preliminary and further high-quality studies with adequate sample sizes are highly recommended.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".