Classic Chinese Acupuncture versus Different Types of Control Groups for the Treatment of Chronic Pain: Review of Randomized Controlled Trials (2000–2018)
Bibliographic record
Abstract
OBJECTIVE: To review the effectiveness of classic Chinese acupuncture in the treatment of chronic pain by comparing treatment groups with different types of control groups in accordance with the newly published guidelines for systematic reviews. METHODS: We searched EMBASE, PubMed, and the Cochrane Central Register of Controlled Trials databases from 2000 to 2018. We included randomized controlled trials that included acupuncture as the sole treatment or as an adjunctive treatment for chronic pain. The outcome was pain intensity measured by the visual analogue scale (VAS), Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain subscale, 11-point numeric rating scale (NRS), and other tools. Two researchers conducted the study selection, data extraction, and quality assessment processes independently. Disagreements were solved by discussion and reanalysis of the data. The quality of all included studies was evaluated using the CBNG (the Cochrane Back and Neck Group) and the STRICTA (Standards for Reporting Interventions in Controlled Trials of Acupuncture) checklists. RESULTS: Sixty-one studies were fully analyzed and ranked based on the newest STRICTA and CBNG standards. We found good evidence that receiving acupuncture is better than not receiving treatment or being placed on a waiting list and reasonable evidence that it is better than conventional or usual care. Limited evidence was found regarding placebo treatments that involve the expectation of needling (real or fake). CONCLUSION: Sham acupuncture may not be appropriate as a control intervention for assessing the effectiveness of acupuncture. Acupuncture effectiveness in controlling chronic pain is still limited due to the low quality of the studies published.
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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 | 0.020 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.013 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".