Efficacy of ozone therapy in dentistry with approach of healing, pain management, and therapeutic outcomes: a systematic review of clinical trials
Bibliographic record
Abstract
Ozone therapy has emerged as a promising treatment modality in dentistry due to its antimicrobial and healing properties. This systematic review aimed to evaluate the recent clinical trials on ozone therapy in dentistry and its impact on therapeutic outcomes. A comprehensive literature search was conducted across multiple databases, including Web of Science, PubMed, and Scopus from January 2018 to December 2024, identifying studies that investigated the use of ozone in dental applications. The findings demonstrated that ozone therapy is effective in improving periodontal health, healing soft tissue after dental implant surgery, and reducing postoperative discomfort. The combination of scaling and root planing with gaseous ozone therapy showed superior periodontal response rates. The use of ozone during endodontics procedures resulted in reduced post-treatment pain, while ozonated materials showed promise in the management of dentinal hypersensitivity. However, it is not recommended in restorative dentistry due to potential adverse effects on dentinal bond strength. The findings of this systematic review supported the integration of ozone therapy into dentistry as an adjunctive therapy. More research is needed to elucidate its mechanisms, optimize application techniques, and evaluate long-term outcomes for patient safety and treatment effectiveness.
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".