Topical Pistacia vera L. Seed Oil Preparation and Its Effects on Knee Osteoarthritis Through a Randomized Double-blind Controlled Clinical Trial
Bibliographic record
Abstract
Background: Osteoarthritis (OA) is a degenerative joint disease that globally affects the elderly, leading to pain and disability. Herbal medications and alternative therapies have demonstrated positive effects on arthritis management. Pistacia vera has traditionally been used for inflammatory conditions and has also shown antinociceptive effects. Objectives: Given the limited available scientific evidence, our randomized controlled trial aimed to assess the potential protective role of topical P. vera seed oil preparation in patients with knee OA. Methods: A total of 89 patients with knee OA (n = 89) were randomly allocated into three groups: Placebo, piroxicam, and P. vera. The topical formulations were administered twice daily over a period of three months. Pain level, patient health status, and performance were evaluated using the visual analog scale (VAS) and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). Statistical analysis was performed using SPSS software. Results: The application of P. vera ointment demonstrated pain reduction in patients, as indicated by VAS and WOMAC assessments. Additionally, WOMAC scores showed that P. vera ointment alleviated motion stiffness and improved activity difficulties in patients (P < 0.001). In certain parameters, the topical application of P. vera showed greater effectiveness in treating knee OA than piroxicam (P < 0.05). Conclusions: Pistacia vera ointment shows promise as a potential therapeutic option for osteoarthritis, effectively addressing the detrimental effects of the disease. Further experimental and clinical studies are warranted to elucidate its efficacy and safety profile.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".