[Warm-needling moxibustion for knee osteoarthritis:a randomized controlled trial].
Bibliographic record
Abstract
OBJECTIVE: To evaluate the clinical efficacy of warm-needling moxibustion for knee osteoarthritis (KOA), and to explore its effects on isokinetic strength of lower limb muscle. METHODS: Fifty cases of KOA were randomly divided into an observation group (25 cases) and a control group (21 cases), but 4 cases lost contact. The observation group was treated with warm-needling moxibustion at Dubi (ST 35), Neixiyan (EX-LE 4), Xuehai (SP 10), Liangqiu (ST 34), Yinlingquan (SP 9), Yanglingquan (GB 34), Weizhong (BL 40), Heyang (BL 55) and Fengshi (GB 31) for 40 min per treatment. The first 6 treatments were given once a day, and the last 6 treatments were given once every other day. 12 treatments were taken as one course, and totally 3-week treatment was given. No treatment was given in the control group for 3 weeks. The isokinetic strength of extensor muscle and flexor muscle, including the total work, absolute peak torque (aPT) and relative peak torque (rPT), and Western Ontario and McMaster Universities Arthritis Index (WOMAC), and comprehensive efficacy were observed and compared in the two groups. RESULTS: >0.05). The total effective rate was 88.0% (22/25) in the observation group. CONCLUSIONS: Warm-needling moxibustion could relieve pain, improve function and muscle balance, strengthen extensor and flexor muscle power, especially extensor, which has superior clinical efficacy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 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".