EFFICACY AND SAFETY OF CURCUMA LONGA EXTRACT IN THE TREATMENT OF OSTEOARTHRITIS: A SYSTEMATIC REVIEW
Bibliographic record
Abstract
Objective: Osteoarthritis (OA) is a chronic disease caused by inflammation of the tissue and bony structure of the joint, which affects more than 235 million people worldwide. Due to the adverse effects caused by the long-term use of standard treatment of OA, the attempt to find natural remedies to treat chronic diseases continues to rise. Curcuma longa is known to have anti-inflammatory effects, which may impact the pathophysiology of OA. While many randomized controlled trials show the efficacy of Curcuma longa extract in the treatment of OA, there has been no comprehensive review of this evidence. Methods: We systematically searched PubMed, Cochrane, Scopus, ProQuest, EBSCOhost, and ScienceDirect for randomized controlled trials that evaluated Curcuma longa extract (CE extract) vs. control (placebo or other therapy). Three trials were identified. Data were then extracted from the studies and summarized descriptively. Results: Across all trials, Curcuma longa therapy was proven to reduce Visual Analog Scale (VAS) and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) scores significantly compared to the control group. Adverse effects were less likely to appear in patients treated with Curcuma longa extract compared to other groups. Conclusion: CL extract is beneficial as an alternative medication for OA treatment, shown by the reduced scores of the Visual Analog Scale (VAS) and WOMAC in all studies we reviewed.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".