A randomized double-blind active-controlled clinical trial on the efficacy of topical basil (Ocimum basilicum) oil in knee osteoarthritis
Bibliographic record
Abstract
Background Basil is a widely used herb in Persian medicine and is gaining recognition as a functional food worldwide. Aim of the study This trial aimed to assess the effectiveness of a traditional formulation of basil oil in comparison with diclofenac gel in treating knee osteoarthritis, considering its established anti-inflammatory, anti-nociceptive, and anti-oxidative properties. Materials and methods One hundred eligible patients were equally randomized to the traditional basil oil (containing sesame oil) and diclofenac gel groups. They used their respective topical treatments thrice daily for 4 weeks. Various measurements were taken at the beginning of the study, 2, and 4 weeks after starting the intervention, including the 8-m walk test, knee pain (based on visual analog scale), flexion angle of the knee joint, analgesic consumption, and the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) questionnaire. Results No significant differences were observed between the basil oil and diclofenac gel groups in any of the measured outcomes. However, significant improvements were noted within each group for most variables. Conclusion Topical application of the traditional formulation of basil oil appears to improve clinical symptoms and certain functional indicators of knee osteoarthritis to a similar extent as diclofenac gel. This suggests that basil oil could be considered an effective management option for this condition. Clinical Trial Registration: https://irct.behdasht.gov.ir/ , identifier IRCT2017081711341N7.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 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 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".