The Effect of Nanocurcumin in Improvement of Knee Osteoarthritis: A Randomized Clinical Trial
Bibliographic record
Abstract
OBJECTIVE: Osteoarthritis is a degenerative disease of the joints. Non-steroidal antiinflammatory drugs (NSAIDs) are being used for the treatment of osteoarthritis. However, their use is limited due to complications, such as gastrointestinal bleeding. Therefore, it is necessary to find alternative treatments for osteoarthritis. Recently, nanomicelle curcumin has been developed to increase the oral bioavailability of curcumin. The aim of this study was to evaluate the effect of nano curcumin on the alleviation of the symptoms of knee osteoarthritis patients. METHODS: In this randomized, double-blind controlled trial, the intervention group was administered 40 mg of nanocurcumin capsule every 12 hours over a period of six weeks, and the control group received the placebo (similar components of nanomicelle curcumin capsules yet without curcumin). In the final analysis, 36 patients in the nanocurcumin group and 35 patients in the placebo group were enrolled. The Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) was filled for patients in their first visit and at the end of six weeks. Differences were statistically significant at P-value < 0.05. RESULTS: There were no significant differences between the two groups regarding gender, age, Kellgren score, and the duration of the disease before the intervention. A significant decrease was observed in the overall score, along with the scores of pain, stiffness and physical activity subscales of the WOMAC questionnaire in patients of the nano curcumin group compared with the placebo group. CONCLUSION: Nanocurcumin significantly improves the symptoms of osteoarthritis patients.
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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 | 0.002 | 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".