The Efficacy of Electroacupuncture in the Treatment of Knee Osteoarthritis: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
This study aims to evaluate the comparative efficacy of electroacupuncture (EA) and analgesics in treating knee osteoarthritis (KOA) and provide evidence-based medical support for EA for the treatment of KOA. Randomized controlled trials from January 2012 to December 2021 are included in electronic databases. The Cochrane risk of bias tool for randomized trials is used to assess the risk of bias in the included studies, while the Grading of Recommendations, Assessment, Development and Evaluation is used to assess the quality of evidence. Statistical analyses are performed using Review Manager V5.4. There are 1616 patients from 20 clinical studies, including 849 patients in the treatment group and 767 patients in the control group. The effective rate in the treatment group is significantly higher than in the control group (p < 0.00001). In the treatment group, Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) stiffness scores are significantly improved as compared to the control group (p < 0.0001). However, EA is similar to analgesics in improving visual analog scale scores and WOMAC subitems such as pain and joint function. EA is effective in treating KOA because it can significantly improve clinical symptoms and quality of life in KOA patients.
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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 | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.022 | 0.026 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".