Effectiveness of acupuncture with tuina on the knee osteoarthritis – a pilot clinical study
Bibliographic record
Abstract
The knee osteoarthritis (KOA) is one of the major causes of disability in adults. The often lack of effectiveness together with the side-effects associated with conventional drugs have made more and more patients to seek treatment for this disease from complementary and alternative medicine. Acupuncture and Tuina massage have been used traditionally in Chinese medicine to treat KOA. This pilot study aims to estimate the efficacy of acupuncture with tuina as a complementary therapy to routine medical care for KOA through clinical observations. The study was carried out in a Chinese medicine clinic of Hertfordshire. Seven patients with KOA diagnosed by their general practitioners and the Chinese medicine practitioner were involved in the study. The patients received 10 sessions treatment of acupuncture with tuina massage over a period of 10 weeks. The Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) and the Visual Analogue Scale (VAS) were used for the patient to self-score the levels of pain, joint stiffness and functions before the treatment at week 0 and after the treatment at Week 5 and Week 10. Over the period of 10 weeks treatment, continued improvements in the knee conditions of the KOA patients were observed. The scores of all WOMAC subscales and VAS significantly decreased at week 5 and 10 following the treatment of acupuncture with Tuina massage (p< 0.05). When compared with the baseline score before the treatment, the total WOMAC score was reduced nearly 30% at week 5 and 56.48% at week 10 after the treatment. No adverse or side effects were observed during the acupuncture and Tuina treatment. In conclusion, the results from this pilot clinical observation suggest that acupuncture combined with Tuina be a safe and effective treatment for KOA. It deserves further comprehensive studies in this area in the future.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".