The efficacy of curcumin in relieving osteoarthritis: A meta‐analysis of meta‐analyses
Bibliographic record
Abstract
Abstract Osteoarthritis (OA) is one of the most prevalent degenerative joint diseases. Several meta‐analyses have shown that curcumin could improve the function of the knee and alleviate pain in OA, while some meta‐analyses demonstrate controversial results. Hence, we assessed curcumin's effects on knee OA in an umbrella meta‐analysis. PubMed, Scopus, Embase, and Web of Science databases were employed to find English‐language meta‐analyses of randomized controlled trials investigating the effect of curcumin supplementation on OA outcomes up to September 2023. The visual analog scale (VAS), Western Ontario and McMaster Universities Arthritis Index (WOMAC) pain, function, and stiffness scales were analyzed. Effect sizes and 95% confidence intervals were utilized to obtain the overall effect size. A random‐effects model was applied to perform the meta‐analysis. Heterogeneity was determined by I2 statistics and the Cochrane Q‐test. The pooled effect of the 11 included meta‐analyses showed that curcumin could significantly decrease the VAS score (weighted mean difference [WMD] and standardized mean difference [SMD]), WOMAC‐total (SMD and WMD), WOMAC‐Function (SMD and WMD), WOMAC‐Pain (SMD), and WOMAC‐Stiffness scores (SMD) (p ≤ 0.001, ≤0.001, ≤0.001, 0.007, ≤0.001, 0.002, ≤0.001, ≤0.001, respectively). The results strongly support curcuminoid supplementation in relieving pain, improving joint mobility and stiffness, and shortening medication usage of OA 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 | 0.021 | 0.033 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.063 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| 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".