Pain management with acupuncture in osteoarthritis: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: The utility of acupuncture in managing osteoarthritis symptoms is uncertain. Trial results are conflicting and previous systematic reviews may have overestimated the benefits of acupuncture. METHODS: Two reviewers independently identified randomized controlled trials (up to May 2014) from multiple electronic sources (including PubMed/Medline, EMBASE, and CENTRAL) and reference lists of relevant articles, extracted data and assessed risk of bias (Cochrane's Risk of Bias tool). Pooled data are expressed as mean differences (MD), with 95% confidence intervals (CI) (random-effects model). RESULTS: We included 12 trials (1763 participants) comparing acupuncture to sham acupuncture, no treatment or usual care. We adjudicated most trials to be unclear (64%) or high (9%) risk of bias. Acupuncture use was associated with significant reductions in pain intensity (MD -0.29, 95% CI -0.55 to -0.02, I2 0%, 10 trials, 1699 participants), functional mobility (standardized MD -0.34, 95% CI -0.55 to -0.14, I2 70%, 9 trials, 1543 participants), health-related quality of life (standardized MD -0.36, 95% CI -0.58 to -0.14, I2 50%, 3 trials, 958 participants). Subgroup analysis of pain intensity by intervention duration suggested greater pain intensity reduction with intervention periods greater than 4 weeks (MD -0.38, 95% CI -0.69 to -0.06, I2 0%, 6 trials, 1239 participants). CONCLUSIONS: The use of acupuncture is associated with significant reductions in pain intensity, improvement in functional mobility and quality of life. While the differences are not as great as shown by other reviews, current evidence supports the use of acupuncture as an alternative for traditional analgesics in patients with osteoarthritis. SYSTEMATIC REVIEW REGISTRATION: CRD42013005405.
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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 | 0.023 | 0.049 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.038 |
| Bibliometrics | 0.009 | 0.008 |
| 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) | 0.003 | 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".