[Warming acupuncture combined with moxibustion at Yongquan (KI 1) for knee osteoarthritis with kidney-marrow deficiency: a randomized controlled trial].
Bibliographic record
Abstract
OBJECTIVE: To compare the clinical effect between warming acupuncture combined with moxibustion at Yongquan (KI 1) and simple warming acupuncture for knee osteoarthritis with kidney-marrow deficiency. METHODS: A total of 66 patients of knee osteoarthritis with kidney-marrow deficiency were randomized into an observation group and a control group, 33 cases in each one. Warming acupuncture was applied at Neixiyan (EX-LE 4), Dubi (ST 35), Zusanli (ST 36) and Xuanzhong (GB 39) on the affected side in both of the groups. In the observation group, mild moxibustion at bilateral Yongquan (KI 1) was adopted additionally. Each treatment lasted for 30 min, 3 times a week (once every other day), and the consecutive 6 weeks of treatment were required. The Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) score (such as joint pain, stiffness and physical function), the amount of joint effusion and the serum contents of interleukin-1β(IL-1β), tumor necrosis factor-α (TNF-α) and high-sensitivity C-reactive protein (hs-CRP) were observed before and after treatment in the two groups. RESULTS: <0.05). CONCLUSION: Warming acupuncture combined with moxibustion at Yongquan (KI 1) can improve joint function, reduce the amount of joint effusion and the contents of inflammatory response indices for knee osteoarthritis with kidney-marrow deficiency. The therapeutic effect of warming acupuncture combined with moxibustion at Yongquan (KI 1) is better than simple warming acupuncture.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 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) | 0.006 | 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".