Non-Pharmacological Interventions for the Treatment of Raynaud’s phenomenon—A Systematic Review
Bibliographic record
Abstract
Objective: The objective of this systematic review is to describe existing literature pertaining to the use of non-pharmacological interventions (NPIs) for the management of primary or secondary Raynaud’s Phenomenon (RP) compared to placebo. Methods: The Cochrane Central Register of Controlled Trials, MEDLINE, and EMBASE were searched from their inception to the present for randomized controlled trials and clinical trials for studies assessing the therapeutic effects NPIs in primary or secondary RP. The studies were screened, and data were extracted by two reviewers. The major outcomes assessed included frequency (per week) and duration (minutes) of attacks and pain. Results: We found 23 parallel or crossover RCTs, 5 of which were not discussed in this review. The categories of NPIs included acupuncture and other needling techniques (n=4), temperature biofeedback (n=4), lasers and electrotherapy (n=5), exercise therapy (n=2), gas therapy (n=1), therapeutic gloves (n=1), and ischemic preconditioning (n=1). Most studies demonstrated trends towards therapeutic benefit; however, there was substantial heterogeneity amongst the studies. Laser therapy had the most consistent evidence; 60% and 75% of the studies reported significant improvements in frequency of attacks per week and pain. Acupuncture therapies had minimal statistically significant benefits and the data for temperature biofeedback were inconsistent and of low quality. Exercise therapy is more recently being explored showing a marked therapeutic benefit for pain. Conclusion: The evidence is limited and inconsistent; however, the studies demonstrated a trend towards therapeutic benefits, with laser and electrotherapy having the most consistent evidence. Further high-quality and multi-center RCTs are required.
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.008 | 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 | 0.001 |
| 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".