Anti Inflammatory Effects of Turmeric (Curcuma longa) on Osteoarthritis
Bibliographic record
Abstract
Osteoarthritis (OA) is a chronic degenerative joint disease managed with non-steroidal antiinflammatory drugs (NSAIDs), but NSAIDs have side effects. Curcumin, the bioactive component of turmeric, is garnering interest for its anti-inflammatory potency and better safety. This study through meta-analysis shows the efficacy of curcumin in reducing OA symptoms compared to NSAIDs and placebo, quantified via the Visual Analog Scale (VAS), the Western Ontario and McMaster Universities Arthritis Index (WOMAC), and inflammatory biomarkers (CRP and ESR). A systematic review that included RCTs that provided SMD for curcumin intervention was identified. The meta-analysis of SMD was conducted with random-effects models and a subgroup analysis examined dosage effects and comparators. Publication bias was assessed using Egger's regression test and Funnel plots. When measuring pain using VAS, curcumin showed a significant reduction over placebo: SMD = -1.73; 95% CI: -2.19 to -1.27, with no heterogeneity: I² = 0%. When comparing curcumin to NSAIDs, curcumin suggested a non-significant trend for pain reduction (VAS SMD = -0.86; 95% CI: -2.35 to 0.63). WOMAC results favored curcumin with low heterogeneity, the absence of publication bias, as assessed using Egger's test, suggests valid overall results. Curcumin showed a greater amount of pain relief and functional improvement in persons with OA than placebo. Although a few trials revealed that NSAIDs were superior to curcumin for pain relief, all WOMAC data favoured curcumin. These findings imply that curcumin has a safer profile and has the potential to be a long-term therapy for OA; more research is required.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".