Efficacy of dietary supplements for treating knee osteoarthritis: a systematic review and network meta-analysis
Bibliographic record
Abstract
Background: Knee osteoarthritis (KOA) stands as a prevalent clinical condition that frequently affects individuals. A growing body of research has highlighted the potential advantages of dietary supplements, including glucosamine and chondroitin, in the management of KOA. Purpose: This study aims to ascertain the most efficacious dietary supplement for KOA, with a specific focus on reducing pain, alleviating stiffness, and enhancing joint function. Methods: We conducted an exhaustive search of multiple databases, including PubMed, Web of Science, Embase, and the Cochrane Library, from inception to May 2023. We specifically focused on randomized controlled trials (RCTs) comparing various dietary supplements with the placebo group within the context of KOA. Assessment of outcomes among these groups relied on the Western Ontario and McMaster University Osteoarthritis Index (WOMAC), with weighted mean differences (WMDs) and associated 95% confidence intervals (CIs) computed. Network meta-analyses were employed to compare outcomes across different supplement groups in comparison with the placebo. The surface under the cumulative ranking curve (SUCRA) was utilized to rank these supplements. Results: Our comprehensive analysis included 22 studies with 2,777 participants in total. The outcomes from our network meta-analysis yielded the following key findings: To reduce the total WOMAC score, the top three interventions were E-OA-7, LParActin, and LcS. For reducing the WOMAC score of pain, the most effective interventions were Aflapin, NEM, and PFP. In addressing the reduction of the WOMAC score of stiffness, NEM, Aflapin, and MSM emerged as the optimal interventions. Finally, for diminishing the WOMAC score of physical function, the most effective interventions were E-OA-7, LParActin, and LcS. Conclusion: In comparison to the placebo, NEM (for stiffness), Aflapin (for pain), and E-OA-07 (for knee function and WOMAC total score) were discerned as the most effective interventions for the treatment of KOA. Clinical trial registration: https://www.crd.york.ac.uk/prospero/.
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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 | 0.018 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.037 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 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".