Effectiveness of Chinese massage therapy (Tui Na) for chronic low back pain: study protocol for a randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Low back pain is a common, disabling musculoskeletal disorder in both developing and developed countries. Although often recommended, the potential efficacy of massage therapy in general, and Chinese massage (tuina) in particular, for relief of chronic low back pain (CLBP) has not been fully established due to inadequate sample sizes, low methodological quality, and subclinical dosing regimens of trials to date. Thus, the purpose of this randomized controlled trial (RCT) is to evaluate the comparative effectiveness of tuina massage therapy versus conventional analgesics for CLBP. METHODS/DESIGN: The present study is a single center, two-arm, open-label RCT. A total of 150 eligible CLBP patients will be randomly assigned to either a tuina treatment group or a conventional drug control group in a 1:1 ratio. Patients in the tuina group receive a 20 minutes, 4-step treatment protocol which includes both structural and relaxation massage, administered in 20 sessions over a period of 4 weeks. Patients in the conventional drug control group are instructed to take a specific daily dose of ibuprofen. The primary outcome measure is the change from baseline back pain and function, measured by Roland-Morris Disability Questionnaire, at two months. Secondary outcome measures include the visual analogue scale, Japanese orthopedic association score (JOAS), and McGill pain questionnaire. DISCUSSION: The design and methodological rigor of this trial will allow for collection of valuable data to evaluate the efficacy of a specific tuina protocol for treating CLBP. This trial will therefore contribute to providing a solid foundation for clinical treatment of CLBP, as well as future research in massage therapy. TRIAL REGISTRATION: This trial was registered with ClinicalTrials.gov of the National Institute of Health on 22 October 2013 (http://NCT01973010).
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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 | 0.052 | 0.046 |
| Meta-epidemiology (narrow) | 0.008 | 0.004 |
| Meta-epidemiology (broad) | 0.016 | 0.008 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.065 | 0.011 |
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".