Preparation and Evaluation of a Brassica nigra Cream Efficacy in Patients with Knee Osteoarthritis, A Double-blinded Randomized Clinical Trial
Bibliographic record
Abstract
Introduction: Knee osteoarthritis (OA), the most common type of age-related degenerative arthritis, affects more than fourteen million people in the United States. Due to the unavailability of an optimum safe, and effective medication for the disease and the global trend toward natural medicaments originating from Complementary and Alternative Medicine (CAM), we aimed to formulate and investigate a topical formulation from Traditional Persian Medicine (TPM) known as Brassica nigra oil in patients with knee OA. Methods: Brassica nigra oil obtained from the direct cold press method was formulated as a topical cream. Thirty patients with knee OA participated in the study one week after receiving an intra-articular injection of Hyaluronic acid 2% as standard treatment. Fifteen patients used 5 g of Brassica nigra cream topically twice daily for one month, while the 15 patients used cold cream as a placebo similarly. Patients were evaluated clinically and were asked to fill in the Visual Analogue Scale (VAS), Western Ontario and McMaster Universities Arthritis Index (WOMAC), and McGill Pain Questionnaire (MPQ) at the beginning and 4, 12 weeks later. Results: Although there were significant differences between VAS, WOMAC, and MPQ between baseline and 4th week within both groups, a significant improvement was observed between the 4th and 12th week just in the drug group. Moreover, there was a remarkable difference between groups in both of the time intervals (p < 0.001). No adverse effects were reported during the study interval. Conclusion: This study confirms the efficacy and safety of Brassica nigra cream in knee OA. Brassica nigra phenolic and flavonoid content might be responsible for analgesic and antiinflammatory properties. This formulation can be considered a natural product in the management of patients with knee OA after more in vitro and in vivo studies.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| 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 | 0.003 |
| 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".