Effect of turmeric products on knee osteoarthritis: a systematic review and network meta-analysis
Bibliographic record
Abstract
Abstract Background Turmeric has traditionally been used to treat various inflammatory conditions, including knee osteoarthritis (OA). There are multiple turmeric preparations available. However, the comparative effectiveness of these products remains unknown. This study aimed to assess the comparative effectiveness of turmeric products for knee OA outcomes by conducting a systematic review and network meta-analysis of randomized, controlled trials (RCTs). Methods PubMed, EMBASE, SCOPUS, and ClinicalTrials.gov databases were searched up to August 2024, identifying RCTs that compared turmeric preparations and/or active comparators versus placebo. The primary outcome measured pain reduction, using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), while secondary outcomes evaluated pain using other tools. Mean differences (MDs) were pooled using a random-effects model, and the concept of minimum clinically important difference (MCID) was considered. Results Seventeen studies were included. All turmeric preparations significantly reduced WOMAC pain. The mean differences (MD, 95% CI) for WOMAC pain reduction were as follows: − 4.01 (–6.22, − 1.80) for conventional curcuminoid preparations (CT) plus active drug comparators (AC, defined as NSAIDs and acetaminophen), − 3.33 (–5.26, − 1.39) for AC, − 3.17 (–5.50, − 0.83) for CT, and − 2.47 (–3.27, − 1.67) for bioavailability-enhanced curcuminoid preparations (BE). The BE preparation also demonstrated a 30% reduction in WOMAC pain compared to placebo, reaching the MCID threshold. The BE + AC combination led to a 70% reduction in VAS pain compared to AC alone. Conclusions All turmeric preparations appear to be effective in reducing knee OA pain when used as monotherapy compared to placebo. However, the certainty of evidence remains low, indicating a need for further research. PROSPERO registration number CRD42023464749. Clinical trial number not applicable.
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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 | 0.016 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.039 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".