Efficacy and side effect of curcumin for the treatment of osteoarthritis: A meta-analysis of randomized controlled trials
Bibliographic record
Abstract
This meta-analysis aimed to confirm the efficacy and safety (side effect) of curcumin for osteoarthritis (OA). Two researchers independently searched the database of Pub Med, EMBASE and Cochrane Library updated to November 2015 to find randomized controlled trials that reported the effect of curcumin on OA. The outcomes of this meta-analysis were Visual analogue scale (VAS), Western Ontario and McMaster Universities Osteoarthritis Index scale (WOMAC) and side effect. Furthermore, the quality assessment was performed with Cochrane Collaboration’s tool. In addition, standardized mean difference (SMD) and 95% confidence interval (CI) were used for the analysis of continuous data, and the risk ratio (RR) and 95% CI were used to analyze dichotomous data. Sensitivity analysis was performed by using Stata 12.0. A total of 5 studies with 599 patients were included in this study. The results showed that curcumin could significantly improve the WOMAC score (SMD=-0.96; 95% CI:-1.81, -0.10; P=0.03) and VAS score of OA patients (SMD=-1.65; 95% CI:-2.11, -1.19). Furthermore, the side effect rate of curcumin treatment was 0.81times higher than that of ibuprofen treatment. Curcumin can treat OA patients effectively, improving WOMAC score and VAS score, and the side effect of curcumin was not higher than that of ibuprofen.
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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 | 0.025 | 0.037 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.026 | 0.072 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".