Topical Nigella Sativa oil versus diclofenac gel for knee osteoarthritis: A randomized open-labeled active-controlled clinical trial
Bibliographic record
Abstract
Osteoarthritis (OA) is the most common cause of pain and disability among older adults. This study aims to compare the effect of topical use of Nigella Sativa (NS) oil and diclofenac gel on pain and function in knee OA (KOA). This randomized clinical trial was performed in a rheumatology clinic. Patients who fulfilled the American College of Rheumatology criteria for OA were selected. The subjects were randomly assigned to apply NS oil or diclofenac gel on the knee joint 4 times a day for 3 weeks. The outcomes, including pain and physical activity, were measured with a visual analog scale and the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). Of the initial 200 KOA patients who were assessed for eligibility, data from 60 patients (30 in each group) were analyzed. The two groups had no significant difference regarding age, sex, and body mass index. Both interventions showed statistically significant within-group differences in terms of the WOMAC subscales of pain, stiffness, function and VAS of pain (P < 0.001). However, there was no significant difference between the groups. Our findings suggested that topical use of NS oil could be as effective as diclofenac gel in reducing pain and stiffness and improving function in KOA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 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 | 0.001 |
| Research integrity | 0.003 | 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".