Treatment of knee osteoarthritis with acupuncture combined with Chinese herbal medicine: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Many studies have demonstrated that acupuncture combined with Chinese herbal medicine (CHM) effectively treats knee osteoarthritis (KOA), with few side effects. However, few systematic reviews have offered evidence-based support. Here we conducted a meta-analysis on the combination of acupuncture with CHM in treating KOA. METHODS: Databases including CNKI, Wanfang, VIP, PubMed, EMBASE, and Cochrane library were systematically searched for articles on the treatment of KOA by acupuncture combined with CHM from the establishment of the database to May 2021. Three researchers independently searched, screened, extracted, and included articles that met the inclusion standards. The primary outcome measure was overall response rate (ORR), and the secondary outcome measures included Visual Analogue Scale (VAS) score, Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) score, and Lysholm score. ORR was a binary variable, while other indicators were continuous variables. The quality of literature was assessed with a modified Jadad scale. The RevMan 5.3 software provided by the Cochrane Collaboration was used for statistical analysis. RESULTS: Thirty-three randomized controlled trials involving 3,954 patients were included. Meta-analysis showed that ORR [odds ratio (OR) =5.41; 95% confidence interval (CI): (4.38, 6.68); P<0.00001], VAS score [mean difference (MD) =-1.86; 95% CI: (-2.44, -1.29); P<0.00001], WOMAC score [MD =-13.05; 95% CI: (-21.70, -4.41); P=0.003], and Lysholm score [MD =10.47; 95% CI: (5.21, 15.72); P<0.0001] in the combination group were significantly superior to those in the control group. DISCUSSION: Compared with acupuncture alone or CHM/Western drug alone, acupuncture combined with CHM can effectively alleviate knee pain, improve knee function, and increase the quality of life. Thus, this combination can be used as a conservative treatment for KOA. However, due to the small number of high-quality articles and possible biases in our analysis, our conclusions need to be further verified in more and higher-quality studies.
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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 | 0.016 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.047 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| 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".