Updated Systematic Review and Meta-Analysis of Acupuncture for Chronic Knee Pain
Bibliographic record
Abstract
Objective To assess the effectiveness and safety of acupuncture for the treatment of chronic knee pain (CKP). Methods We searched the MEDLINE, EMBASE, Cochrane CENTERAL, CINAHL and four Chinese medical databases from their inception to June 2017. We included randomised controlled trials of acupuncture as the sole treatment or as an adjunctive treatment for CKP. The primary outcome was pain intensity measured by visual analogue scale (VAS), Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain subscale and 11-point numeric rating scale. Secondary outcome measurements included the 36-Item Short Form Health Survey and adverse events. The quality of all included studies was evaluated using the Cochrane risk-of-bias criteria and the STRICTA (Standards for Reporting Interventions in Controlled Trials of Acupuncture) checklist. Results Nineteen trials were included in this systematic review. Of these, data from 17 studies were available for analysis. Regarding the effectiveness of acupuncture alone or combined with other treatment, the results of the meta-analysis showed that acupuncture was associated with significantly reduced CKP at 12 weeks on WOMAC pain subscale (mean difference (MD) −1.12, 95% confidence interval (CI) −1.98 to −0.26, I 2 =62%, 3 trials, 608 participants) and VAS (MD −10.56, 95% CI −17.69 to −3.44, I 2 =0%, 2 trials, 145 patients). As for safety, no difference was found between the acupuncture and control groups (risk ratio 1.08, 95% CI 0.54 to 2.17, I 2 =29%). Conclusion From this systematic review, we conclude that acupuncture may be effective at relieving CKP 12 weeks after acupuncture administration, based on the current evidence and our protocol. However, given the heterogeneity and methodological limitations of the included trials, we are currently unable to draw any strong conclusions regarding the effectiveness of acupuncture for chronic knee pain. In addition, we found that acupuncture appears to have a satisfactory safety profile, although further studies with larger numbers of participants are needed to confirm the safety of this technique. Strengths Systematic review without language restrictions. Limitations Only a few high-quality and consistent trials could be included in this review.
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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 | 0.017 | 0.048 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.029 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".