Efficacy and side effect of curcumin for the treatment of osteoarthritis: A meta-analysis of randomized controlled trials.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.014 | 0.004 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it