The efficacy of high- and low-dose curcumin in knee osteoarthritis: A systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to critically appraise and evaluate effects of low- and high-dose curcuminoids on pain and functional improvement in patients with knee osteoarthritis (OA) and to compare adverse events (AEs) between curcuminoids and non-steroid anti-inflammatory drugs (NSAIDs). METHODS: We systematically reviewed all randomized controlled trials (RCTs) on curcuminoids in knee osteoarthritis from the PubMed, Embase, Cochrane Library, AMED, Cinahl, ISI Web of Science, Chinese medical database, and Indian Scientific databases from inception to June 21, 2021. RESULTS: We included eleven studies with a total of 1258 participants with primary knee OA. The meta-analysis results showed that curcuminoids were significantly more effective than comparators regarding visual analogue scale (VAS) and Western Ontario and McMaster Universities Arthritis Index (WOMAC) pain scores. However, no significant difference in pain relief or AEs between the high-dose (daily dose ≥1000 mg or total dose ≥42 gm) and low-dose (daily dose <1000 mg or total dose <42 gm) curcuminoid treatments was observed. When comparing curcumininoids versus NSAIDs, a significant difference in VAS pain was found. For AE analysis, three of our included studies used NSAIDs as comparators, with all reporting higher AE rates in the NSAID group, though significance was reached in only one study. CONCLUSIONS: The results of our meta-analysis suggest that low- and high-dose curcuminoids have similar pain relief effects and AEs in knee OA. Curcuminoids are also associated with better pain relief than NSAIDs; therefore, using curcuminoids as an adjunctive treatment in knee OA is recommended.
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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 | 0.020 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.039 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".