The use of acupuncture in patients with Raynaud’s syndrome: a systematic review and meta-analysis of randomized controlled trials
Bibliographic record
Abstract
OBJECTIVE: To assess the effectiveness of acupuncture for the treatment of Raynaud's syndrome by conducting a systematic review and meta-analysis of randomized controlled trials (RCTs). METHODS: Studies were identified from English and Chinese databases from their inception to September 2020. The outcomes of interest were remission incidence, number of daily attacks, incidence of positive cold stimulation tests and incidence of cold provocation tests. We conducted meta-analysis and network meta-analysis using meta and gemtc. RESULTS: Six trials (n = 272 participants) were included in the meta-analysis. Pairwise meta-analyses show that acupuncture was associated with increased remission incidence (risk ratio (RR) = 1.21, 95% confidence interval (CI) = 1.10 to 1.34), decreased daily number of attacks (weighted mean difference (WMD) = -0.57, 95% CI = -1.14 to -0.01), and increased incidence of positive cold stimulation tests (RR = 1.64, 95% CI = 1.27 to 2.11). There was not enough evidence to associate acupuncture with decreased incidence of positive cold provocation tests. The network meta-analyses did not demonstrate significant results for the effectiveness of any acupuncture treatments (electroacupuncture or manual acupuncture ± moxibustion), compared with controls, in terms of remission incidence or daily number of attacks, possibly due to small sample sizes and a lack of statistical power. CONCLUSION: The use of acupuncture may be effective for the treatment of Raynaud's syndrome in terms of increasing remission incidence, decreasing daily number of attacks and increasing incidences of positive cold stimulation tests. However, our findings should be interpreted with caution due to small sample sizes, very low quality of evidence and high risk of bias. Future large-scale RCTs are warranted.
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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 | 0.014 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.035 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".