Efficacy and Safety of External Therapies of Traditional Chinese Medicine in Patients with Knee Osteoarthritis: A Systematic Review and Network Meta-Analysis
Bibliographic record
Abstract
The use of external therapies for knee osteoarthritis (KOA) in traditional Chinese medicine (TCM) is supported by several guidelines and systematic reviews. However, the relative advantages and disadvantages of TCM external therapies and their mechanisms of action have not yet been confirmed in evidence-based medicine. We used network meta-analysis to rank the effectiveness and safety of TCM external therapies, screen the optimal TCM external therapies. TCM external therapies for KOA published before October 2024 were comprehensively retrieved from eight electronic databases. Using the Cochrane Reviewers' Handbook, two independent reviewers performed study selection, data extraction, and bias assessment of the included randomized controlled trials (RCTs). Data analysis was conducted using Stata 16.0 and RevMan 5.4 software. A total of 68 RCTs were identified, including 6571 participants, involving 11 interventions, 4.41% of which showed a high risk of bias. The results of the network meta-analysis revealed that in terms of improving Visual Analog Scale (VAS) and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) function scores, each external therapy was better than conventional medicine. Electroacupuncture may be the most effective intervention in improving the VAS score and TNF-α level. Moxibustion resulted in the greatest improvement in WOMAC function and IL-6 levels. The most effective interventions for reducing WOMAC pain scores were the manual needle knife, followed by electroacupuncture and Tuina therapy (SUCRA = 82.9%, 79.0%, and 71.4%, respectively). Warming acupuncture dominantly increased Lysholm scores. The safety results showed that the three safest interventions were the sham intervention, Tuina therapy, and moxibustion (SUCRA = 90.6%, 83.1%, and 68.8%, respectively). Silver needle had the best comprehensive effect. Electroacupuncture has the best effect on improving pain symptoms, and moxibustion can be prioritized when functional limitations are the main symptoms. To some extent, the changes in inflammatory factors correlated with an improvement in KOA symptoms.
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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 | 0.023 | 0.044 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.044 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".