Topical-Ozonized Olive Oil – A Boon for Post-Extraction Cases: A Randomized Controlled Trial
Bibliographic record
Abstract
Background Post-surgical therapy in exodontia patients has historically been largely centered on pain and infection prevention. Healing of the extraction wound has rarely received any importance during regular dental extractions, despite being an inherent element of the process of tooth extraction itself. This study aimed to analyze the analgesic and antibacterial efficacy of topical-ozonized olive oil compared to regular drugs administered post-operatively to patients who have undergone tooth extraction as well as evaluate the healing effects of the former on the extraction site. Methodology A total of 200 patients in need of exodontia were randomly divided into two groups, with group A (case group) receiving ozonized olive oil as a topical application for three days and group B (control group) receiving standard post-operative treatment (antibiotics and analgesics). On day five, patients in both groups were assessed for wound healing using the Landry, Turnbull, and Howley Index and for pain using the visual analog scale (VAS). Results On days two and three, the P-value for differences in pain (VAS score) between the two groups was 0.409, but on day five, it was 0.180. According to the Landry, Turnbull, and Howley index, the P-value for differences in wound healing between the groups on day five was 0.025. When comparing the two groups, there was no discernible difference in the amount of discomfort perceived after surgery. While both groups saw improvement in wound healing and pain, the case group coped better than the control group in terms of wound healing. Conclusions This study demonstrated that ozonized olive oil may be used as a safe and effective alternative to conventional painkillers and antibiotics and can speed up wound healing after exodontia.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".