Efficacy and safety of moxibustion for knee osteoarthritis: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Although several reviews have examined moxibustion for knee osteoarthritis (KOA), they have some methodological limitations. This systematic review aims to synthesize current evidence on various moxibustion interventions for KOA pain relief. METHODS: We conducted a comprehensive search across seven databases up to June 2024. The Cochrane risk of bias tool (ROB2) was used to assess the risk of bias, and the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) criterion approach was employed to evaluate the certainty of evidence for primary outcomes. Primary outcomes included Visual Analogue Scale (VAS), Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), stiffness, physical function, and total effective rate. Secondary outcomes encompassed adverse events (AEs) and treatment regimen of moxibustion for KOA. Meta-analysis was performed using STATA 17.0 and RevMan 5.4.1. RESULTS: A total of 43 studies with 4008 participants were included in this review. Moxibustion significantly reduced WOMAC (-0.91, 95 % CI: -1.12, -0.70) and VAS scores (-0.95, 95 % CI: -1.18, -0.73). Electronic moxibustion was the most effective intervention. Treatment frequencies exceeding three times per week were more effective than fewer sessions. The total effective rate was 93.11 % for the moxibustion group versus 76.41 % for the controls. AEs were reported in sixteen studies, with one serious AE. CONCLUSION: Moxibustion is more effective for pain relief than other treatments and has a lower incidence of AEs. Further high-level evidence is warranted to validate these findings.
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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 | 0.015 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.037 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".