Hyaluronic Acid in Dentistry: A Narrative Review
Bibliographic record
Abstract
Biocompatibility, anti-inflammatory properties, and regenerative potential. It plays a crucial role in enhancing wound healing, reducing inflammation, and supporting tissue repair. This review aims to evaluate the clinical efficacy of HA in various dental applications, including periodontal therapy, oral surgery, implantology, and the management of oral mucosal lesions. A systematic literature review was conducted following PRISMA guidelines. Searches were performed in PubMed, Scopus, Web of Science, and Cochrane Library databases, focusing on studies published in the last 5 years. Inclusion criteria comprised clinical trials, cohort studies, and systematic reviews assessing HA’s effects on oral lichen planus, oral ulcers, periodontal disease, and post-surgical healing. Studies were evaluated for quality using the Cochrane Risk of Bias Tool and the Newcastle-Ottawa Scale. The review identified five key studies demonstrating HA’s beneficial effects in dentistry. HA was shown to reduce pain and lesion size in oral lichen planus, enhance postoperative healing following third molar extractions, and improve alveolar ridge preservation when combined with demineralized bovine bone. Additionally, HA gel applications accelerated gingival healing post-gingivectomy, and its combination with photobiomodulation therapy further optimized wound repair. HA has proven to be a valuable adjunct in various dental treatments, promoting tissue regeneration and reducing postoperative complications. Its combination with other biomaterials and regenerative therapies enhances its clinical efficacy. However, further research is needed to standardize its application protocols and assess long-term outcomes. As advancements in biomaterials continue, HA is poised to play an increasingly significant role in modern dentistry.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".