Impact of Hyaluronic Acid Around Dental Implants: A Systematic Review
Bibliographic record
Abstract
A bstract Aim: Hyaluronic acid (HA), a naturally occurring glycosaminoglycan, has gained attention in dental implantology due to its bioactive, anti-inflammatory, and wound-healing properties. This systematic review evaluates the current evidence regarding the application and efficacy of HA around dental implants, focusing on its impact on peri-implant tissue health, osseointegration, and prevention of peri-implant diseases. Materials and Methods: A comprehensive search was conducted across PubMed, Scopus, Embase, Cochrane Library, Google Scholar, and Web of Science databases for studies published from 2016 to 2024. Inclusion criteria: Encompassed human studies, animal studies, and in vitro studies. The Cochrane risk of bias 2 tool and the Newcastle–Ottawa Scale were used to assess the risk of bias. A total of 12 studies met the inclusion criteria. Results: It is demonstrated that HA application positively influences soft tissue healing, reduces inflammation, and may support early osseointegration. Despite promising findings, heterogeneity in HA formulations, application protocols, and follow-up durations limits the ability to draw definitive conclusions. Further long-term, standardized clinical trials are needed to validate its routine use in implant dentistry. Conclusion: This review highlights HA’s potential as a valuable adjunct in implant therapy, especially for enhancing peri-implant tissue health and managing early peri-implant disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 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".