Clinical Effectiveness of Aloe Vera in the Management of Oral Mucosal Diseases- A Systematic Review
Bibliographic record
Abstract
INTRODUCTION: Aloe vera is well known for its medicinal properties which lead to its application in treating various diseases. Its use in treating oral lesions has not been much documented in literature. AIM: Although, systematic reviews on aloe vera and its extracts have been done earlier, but in relation to oral diseases this is the first systematic review. The aim of the present systematic review was to compile evidence based studies on the effectiveness of Aloe vera in treatment of various oral diseases. MATERIALS AND METHODS: Computerized literature searches were performed to identify all published articles in the subject. The following databases were used: PUBMED [MEDLINE], SCOPUS, COCHRANE DATABASE, EMBASE and SCIENCE DIRECT using specific keywords. The search was limited to articles published in English or with an English Abstract. All articles (or abstracts if available as abstracts) were read in full. Data were extracted in a predefined fashion. Assessment was done using Jadad score. RESULTS: Fifteen studies satisfied the inclusion criteria. Population of sample study ranged from 20 patients to 110 patients with clinically diagnosed oral mucosal lesions. Out of 15 studies, five were on patients with oral lichen planus, two on patients with oral submucous fibrosis, other studies were carried on patients with burning mouth syndrome, radiation induced mucositis, candida associated denture stomatitis, xerostomic patients and four were on minor recurrent apthous stomatitis. Most studies showed statistically significant result demonstrating the effectiveness of Aloe vera in treatment of oral diseases. CONCLUSION: Although there are promising results but in future, more controlled clinical trials are required to prove the effectiveness of Aloe vera for management of oral diseases.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".