Sinew acupuncture for knee osteoarthritis: study protocol for a randomized sham-controlled trial
Bibliographic record
Abstract
BACKGROUND: Sinew acupuncture is a new modality of acupuncture in which needles are inserted into acupoints, ashi points or spasm points of sinew and muscles along the meridian sinew pathway. A previous observational study revealed that sinew acupuncture has immediate analgesic effects on various soft tissue injuries, including knee injuries. However, no rigorous trials have been conducted. This study aims to examine whether sinew acupuncture can safely relieve pain and symptoms of knee osteoarthritis (KOA) and improve patients' functional movement and quality of life. METHODS/DESIGN: A randomized, sham-controlled, patient- and assessor-blinded trial will be conducted to compare the efficacy of sinew acupuncture and sham acupuncture. Subjects will be assessed by the physician and acupuncturists. A sample of eighty-six eligible subjects will be randomized into either the sinew acupuncture group or the sham acupuncture group. The intervention will be performed in the Hong Kong Tuberculosis Association Chinese Medicine Clinic cum Training Centre of the University of Hong Kong by acupuncturists with over 3 years of acupuncture experience. Subjects will receive 10 sessions of interventions for 4 weeks, followed by a 6-week follow-up. The visual analogue scale (VAS) score at week 4 will be the primary outcome. The Western Ontario and McMasters University Osteoarthritis Index (WOMAC), Timed Up & Go Test (TUG), 8-step Stair Climb Test (SCT) and the 36-Item Short Form Survey (SF-36) will be secondary outcomes. DISCUSSION: Sinew acupuncture is a potential alternative non-pharmacological therapy for KOA. This rigorous trial will expand our knowledge of whether sinew acupuncture reduces pain intensity and improves symptoms, functional movements, and quality of life of KOA patients. TRIAL REGISTRATION: The study was registered at ClinicalTrials.gov (Identifier: NCT03099317) in March 2017.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.018 |
| Meta-epidemiology (narrow) | 0.006 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.005 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.079 | 0.010 |
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".