Effect of acupotomy combined with electroacupuncture on knee function of patients with knee osteoarthritis.
Bibliographic record
Abstract
OBJECTIVES: To compare the clinical effect of combined therapy of acupotomy and electroacupuncture (EA) with the simple application of EA on knee osteoarthritis (KOA), and their influence on knee function. METHODS: Sixty-eight KOA patients were randomly divided into 2 groups, an acupotomy group and an EA group. In the acupotomy group, the combined therapy of acupotomy and EA was adopted. In the EA group, EA was simply used, delivered once every two days, 3 treatments a week；and the duration of treatment was 4 weeks. In the acupotomy group, besides the treatment as the EA group, acupotomy was combined once weekly, and the duration of treatment was 4 weeks. Separately, before and after treatment, and in 4 and 12 weeks after treatment completion (1-month and 3-month follow-up), the results of the timed up and go test (TUG), the 9-step stair climb test (9-SCT) and the knee function (Western Ontario and McMaster University osteoarthritis index visualization scale ［WOMAC］) were measured in the two groups. RESULTS: <0.05). CONCLUSIONS: Either the simple application of EA or the combined therapy of acupotomy and EA can improve knee function, but the combined therapy obviously increases the walking speed and relieves the symptoms such as joint pain and morning stiffness. The treatment with acupotomy and EA is safe and effective on KOA and the long-term effect is satisfactory.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".