The Effect of Acupuncture on the Symptoms of Knee Osteoarthritis - An Open Randomised Controlled Study
Bibliographic record
Abstract
BACKGROUND: Using an open randomised controlled study, we examined the effectiveness of manual and electroacupuncture on symptom relief for patients with osteoarthritis of the knee. METHODS: Patients with symptomatic osteoarthritis of the knee were randomised to one of three treatment groups. Group A had acupuncture alone, group B had acupuncture but continued on their symptomatic medication, and group C used their symptomatic medication for the first five weeks and then had a course of acupuncture added. Patients receiving acupuncture were treated twice weekly over five weeks. Needles were inserted (with manual and electrical stimulation) in acupuncture points for pain and stiffness, selected according to traditional acupuncture theory for treating Bi syndrome. Patients were assessed by a blinded observer before treatment, after five weeks' treatment and at one month follow up, using a visual analogue pain scale (VAS) and the Western Ontario McMaster (WOMAC) questionnaire for osteoarthritis of the knee. RESULTS: The 30 patients in our study were well matched for age, body mass index, disease duration, baseline VAS pain score and baseline WOMAC scores. Repeated measure analyses gave a highly significant improvement in pain (VAS) after the courses of acupuncture in groups A (P = 0.012) and B (P=0.001); there was no change in group C until after the course of acupuncture, when the improvement was significant (P = 0.001). Similarly significant changes were seen with the WOMAC pain and stiffness scores. These benefits were maintained during the one month after the course of acupuncture. Patients' rating of global assessment was higher than that of the acupuncturist. CONCLUSION: We conclude that manual and electroacupuncture causes a significant improvement in the symptoms of osteoarthritis of the knee, either on its own or as an adjunct therapy, with no loss of benefit after one month.
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".