Comparative efficacy of aloe vera mouthwash and chlorhexidine on periodontal health: A randomized controlled trial
Bibliographic record
Abstract
Background: With introduction of many herbal medicines, dentistry has recently evidenced shift of approach for treating many inflammatory oral diseases by using such modalities.Aloe vera is one such product exhibiting multiple benefits and has gained considerable importance in clinical research recently.Aims: To compare the efficacy of Aloevera and Chlorhexidine mouthwash on Periodontal Health.Material and Methods: Thirty days randomized controlled trial was conducted among 390 dental students.The students were randomized into two intervention groups namely Aloe Vera (AV) chlorhexidine group (CHX) and one control (placebo) group.Plaque index and gingival index was recorded for each participant at baseline, 15 days and 30 days.The findings were than statistically analyzed, ANOVA and Post Hoc test were used.Results: There was significant reduction (p<0.05) in the mean scores of all the parameters with Aloe Vera (AV) and chlorhexidine group.Post hoc test showed significant difference (p<0.000) in mean plaque and gingival index scores of aloe Vera and placebo and chlorhexidine and placebo group.No significant difference (p<0.05) was observed between AloeVera and chlorhexidine group.Conclusions: Being an herbal product AloeVera has shown equal effectiveness as Chlorhexidine.Hence can be used as an alternative product for curing and preventing gingivitis.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".