[Clinical Studies of Abdominal Acupuncture Combined with Warm Needling on Knee Osteoarthritis].
Bibliographic record
Abstract
OBJECTIVE: To explore the clinical effects of abdominal acupuncture combined with warm needling on knee osteoarthritis (KOA). METHODS: Eighty-six patients with KOA were divided into a treatment group and a control group according to the digital random table, 43 cases in each one. In the treatment group, patients were treated with abdominal acupuncture at Zhongwan (CV 12), Guanyuan (CV 4) and Huaroumen (ST 24), Wailing (ST 26) and Xiafengshidian (Extra) on the affected side, as well as warm needling at Neixiyan (EX-LE 4), Dubi (ST 35), Heding (EX-LE 2), Liangqiu (ST 34) and Xuehai (SP 10) of the affected side. In the control group, warm needling was given. All the patients were treated 5 times a week for 4 weeks. Each dimension score of Western Ontario and McMaster University (WOMAC) osteoarthritis index scale and health survey 36-item short form (SF-36) of the two groups was compared before and after treatment. Enzyme-linked immunosorbent assay was applied to test serum vascular endothelial growth factor (VEGF) and angiopoietin-1(Ang-1). RESULTS: <0.05). CONCLUSIONS: Abdominal acupuncture combined with warm needling can effectively alleviate pain and stiffness, improve the function of knee joint and quality of life, with definite effect for KOA. The mechanism may be related to the decreasing of serum VEGF and Ang-1.
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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 | 0.001 | 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) | 0.003 | 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".