Evaluation of wet-cupping therapy for persistent non-specific low back pain: a randomised, waiting-list controlled, open-label, parallel-group pilot trial
Bibliographic record
Abstract
BACKGROUND: Persistent non-specific low back pain (PNSLBP) is one of the most frequently experienced types of back pain around the world. Wet-cupping is a common intervention for various pain conditions, especially in Korea. In this context, we conducted a pilot study to determine the effectiveness and safety of wet-cupping treatment for PNSLBP. METHODS: We recruited 32 participants (21 in the wet-cupping group and 11 in the waiting-list group) who had been having PNSLBP for at least 3 months. The participants were recruited at the clinical research centre of the Korea Institute of Oriental Medicine, Korea. Eligible participants were randomly allocated to wet-cupping and waiting-list groups. Following the practice of traditional Korean medicine, the treatment group was provided with wet-cupping treatment at two acupuncture points among the BL23, BL24 and BL25 6 times within 2 weeks. Usual care, including providing brochures for exercise, general advice for PNSLBP and acetaminophen, was allowed in both groups. Separate assessors participated in the outcome assessment. We used the 0 to 100 numerical rating scale (NRS) for pain, the McGill Pain Questionnaire for pain intensity (PPI) and the Oswestry Disability Questionnaire (ODQ), and we assessed acetaminophen use and safety issues. RESULTS: The results showed that the NRS score for pain decreased (-16.0 [95% CI: -24.4 to -7.7] in the wet-cupping group and -9.1 [-18.1 to -0.1] in the waiting-list group), but there was no statistical difference between the groups (p = 0.52). However, the PPI scores showed significant differences between the two groups (-1.2 [-1.6 to -0.8] for the wet-cupping group and -0.2 [-0.8 to 0.4] for the waiting-list group, p < 0.01). In addition, less acetaminophen was used in the wet-cupping group during 4 weeks (p = 0.09). The ODQ score did not show significant differences between the two groups (-5.60 [-8.90 to -2.30] in the wet-cupping group and -1.8 [-5.8 to 2.2] in the waiting-list group, p = 0.14). There was no report of adverse events due to wet-cupping. CONCLUSION: This pilot study may provide preliminary data on the effectiveness and safety of wet-cupping treatments for PNSLBP. Future full-scale randomised controlled trials will be needed to provide firm evidence of the effectiveness of this intervention.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".