Efficacy of Acupuncture-Related Therapy in the Treatment of Knee Osteoarthritis: A Network Meta-Analysis of Randomized Controlled Trials
Bibliographic record
Abstract
OBJECTIVE: Knee osteoarthritis (KOA) is prevalent in middle-aged and elderly people. This condition negatively affects the quality of life of patients. Although non-steroidal anti-inflammatory drugs (NSAIDs) are often used to relieve symptoms associated with KOA, it is associated with many side effects. Acupuncture and moxibustion therapies have been applied in the treatment of KOA. However, the efficacy of various acupuncture and moxibustion treatments has not been compared. METHODS: Randomized controlled trials (RCTs) on the application of acupuncture and moxibustion in the treatment of KOA were searched in English databases and Chinese databases. Data were retrieved from establishment of the database to September 2020. Data analysis was performed using Stata14.0 and GeMTC 0.14.3 softwares. RESULTS: A total of 40 RCTs involving 3215 patients with KOA were retrieved. Network meta-analysis revealed that the fire needle was superior to western medicine, electro-acupuncture, conventional acupuncture, warm needle and sham acupuncture; warm needle was better than conventional acupuncture and western medicine whereas electro-acupuncture was better than conventional acupuncture in improving pain scores in the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). Moreover, we found that fire needle and warm needle more effectively improved WOMAC stiffness scores than western medicine and sham moxibustion, whereas electro-acupuncture was superior to western medicine and sham moxibustion in improving WOMAC stiffness scores. Further analysis revealed that fire needle, warm needle and electro-acupuncture were more effective in improving WOMAC joint function scores than conventional acupuncture and western medicine. The fire needle was superior to conventional acupuncture and sham acupuncture, whereas electro-acupuncture was better than western medicine, conventional acupuncture and sham acupuncture in improving visual analogue scale scores. CONCLUSION: This study shows that fire needle is superior to warm needle and electro-acupuncture, whereas warm needle and electro-acupuncture are better than conventional acupuncture, western medicine, sham moxibustion and sham acupuncture.
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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 | 0.025 | 0.061 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.023 | 0.041 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 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".