Efficacy of botanical extracts for knee osteoarthritis: a network meta-analysis of randomized controlled trials
Bibliographic record
Abstract
Background: Traditional botanical drugs and medicinal plants, along with their metabolite extracts, have exhibited considerable potential in the management of knee osteoarthritis (KOA) due to their natural properties, favorable safety profiles, and minimal adverse effects. Objective: This study aimed to evaluate the therapeutic efficacy of various botanical and medicinal plant extracts on KOA. Search Methods: We conducted a comprehensive literature search across PubMed, Embase, Cochrane Library, and Web of Science, focusing exclusively on randomized controlled trials (RCTs) that investigated the efficacy of botanical and medicinal plant extracts for KOA. Selection Criteria: Studies were included if they met the following criteria: (1) experimental groups receiving single botanical drugs or plant extracts for KOA; (2) control groups comprising patients receiving placebo or standard care; (3) clinical RCT designs; and (4) outcome measures including at least one of the following: Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), Visual Analogue Scale (VAS), Short Form 36 Health Survey (SF-36), Knee injury and Osteoarthritis Outcome Score (KOOS), Lequesne's Pain-Function Index (LPFI), Japanese Orthopaedic Association Score (JOA). Data Collection and Analysis: The methodological quality of the included studies was assessed using the Cochrane risk of bias tool, and data analysis was performed using appropriate statistical software. Results: A total of 36 RCTs, encompassing 3,285 participants, were included in this review. Network meta-analysis revealed that compared to the placebo control group, Cucumis sativus (CS) extract [MD = 6.65, 95% CI = (3.83, 9.48)] significantly improved pain scores; Ashwagandha extract [MD = 4.16, 95% CI = (2.43, 5.90)] was more effective in reducing stiffness scores; and CS extract [MD = 4.28, 95% CI = (2.08, 6.49)] significantly improved function scores. Conclusion: Based on Ranking Plot of the Network, we can state that CS extract is recommended as the most effective botanical and medicinal plant extract for KOA treatment. However, further studies are required to draw definitive conclusions. Given that there are only two studies with high homogeneity but small sample size for CS extract, the first result should be regarded as an exploratory signal and needs to be verified by a large sample multi-center RCT with independent teams. Clinical Trial Registration: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42024617459, identifier CRD42024617459.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.028 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.107 | 0.030 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".