Effect of Aloe vera mouthwash on periodontal health: triple blind randomized control trial.
Bibliographic record
Abstract
BACKGROUND: With the increasing incidence of periodontal diseases and development of antibiotic resistance, the global need for alternative treatment modalities, safe, effective, and economical products is the need of time. Aloe vera is a medicinal plant which has the greater medicinal value and enormous properties for curing and preventing oral diseases disease. AIM: The aim of the study was to access the effect of Aloe vera mouthwash on the dental plaque and gingivitis and comparing it with the bench mark control chlorhexidine and placebo. MATERIAL AND METHODS: 345 healthy subjects were randomly allocated in 3 groups to the test group (n=115) - mouthwash containing Aloe vera, Control group (n=115) -chlorhexidene group, Distilled water - Placebo (n=115) . Plaque Index (PI) and Gingival Index (GI) were assessed at days 0, 15 and 30. Subjects were asked to rinse their mouth with the stated mouthwash, twice a day, during a 30-day period. RESULTS: Our result showed that Aloe vera mouthrinse is equally effective in reducing periodontal indices as Chlorhexidine. The results demonstrated a significant reduction of gingival bleeding and plaque indices in both groups over a period of 15 and 30 days as compared to placebo group. There was a significant reduction on plaque and gingivitis in Aloe vera and chlorhexidine groups and no statistically significant difference was observed among them (p>0.05). Aloe vera mouthwash showed no side effects as seen with chlorhexidine. CONCLUSION: The results of the present study indicate that Aloe vera may prove to be an effective mouthwash owing to its ability in reducing periodontal indices.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".