Curcuminoid Treatment for Knee Osteoarthritis: A Randomized Double‐Blind Placebo‐Controlled Trial
Bibliographic record
Abstract
Treatment of osteoarthritis (OA) is challenging owing to the inefficacy and long-term adverse events of currently available medications including non-steroidal anti-inflammatory drugs. Curcuminoids are polyphenolic phytochemicals with established anti-inflammatory properties and protective effects on chondrocytes. The aim of this study is to investigate the clinical efficacy of curcuminoids in patients suffering from knee OA. A pilot randomized double-blind placebo-control parallel-group clinical trial was conducted among patients with mild-to-moderate knee OA. Patients were assigned to curcuminoids (1500 mg/day in 3 divided doses; n = 19) or matched placebo (n = 21) for 6 weeks. Efficacy measures were changes in Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), visual analogue scale (VAS) and Lequesne's pain functional index (LPFI) scores during the study. There was no significant difference in age, gender, body mass index, and VAS, WOMAC and LPFI scores between the study groups at baseline (p > 0.05). Treatment with curcuminoids was associated with significantly greater reductions in WOMAC (p = 0.001), VAS (p < 0.001) and LPFI (p = 0.013) scores compared with placebo. With respect to WOMAC subscales, there were significant improvements in the pain and physical function scores (p < 0.001) but not stiffness score (p > 0.05). There was no considerable adverse effect in both groups. To conclude, curcuminoids represent an effective and safe alternative treatment for OA.
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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 | 0.003 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".