The efficacy of complementary and alternative medicine in the treatment of Raynaud's phenomenon: a literature review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: Conventional treatment for RP is limited due to side effects, and complementary and alternative medicines (CAM) are widely used by the population. Our objective was to find an effective and well-tolerated CAM for the treatment of RP. METHODS: Using MEDLINE, EMBASE and AMED, 20 randomized controlled trials (RCTs) were found and divided into nine treatment subcategories: acupuncture (n = 2 trials), anti-oxidants (n = 2), biofeedback (n = 5), essential fatty acids (n = 3), Ginkgo biloba (n = 1), L-arginine (n = 2), laser (n = 3), glucosaminoglycans (n = 1) and therapeutic gloves (n = 1). Trials in each subcategory were meta-analysed together. RESULTS: Several categories did not have enough trials to do a meta-analysis and most trials were negative, of poor quality and done prior to 1990. Biofeedback was negative for a change in frequency, duration and severity of RP attacks, and actually favoured control (sham biofeedback; P < 0.02). The therapeutic glove favoured active treatment (P < 0.00001). Laser resulted in one less RP attack on average over 2 weeks vs sham [weighted mean difference (WMD) 1.18; 95% CI 1.06, 1.29], and a change in severity of attacks (WMD 1.98; 95% CI 1.57, 2.39; P < 0.05). No significant differences were found in the nutritional supplements that were studied. CONCLUSIONS: There is a need for well-designed trials of CAM in RP. The literature is inconclusive except that biofeedback does not work for RP, therapeutic gloves may improve RP (but results may not be generalizable due to single trial site and no intent-to-treat analysis) and laser may be effective but the improvement may not be clinically relevant.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.039 |
| Bibliometrics | 0.010 | 0.010 |
| 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) | 0.005 | 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".