[Governor vessel moxibustion combined with warming acupuncture for knee osteoarthritis with <i>yang</i> deficiency and cold congelation based on the supporting-<i>yang</i> theory].
Bibliographic record
Abstract
OBJECTIVE: deficiency and cold congelation. METHODS: deficiency and cold congelation were randomized into a combination group (32 cases, 2 cases dropped off) and a warming acupuncture group (32 cases, 1 case dropped off). In the warming acupuncture group, warming acupuncture was applied at Zusanli (ST 36), Guanyuan (CV 4) and Dubi (ST 35), Neixiyan (EX-LE 4), etc. on the affected side, once a day. On the basis of the treatment in the warming acupuncture group, governor vessel moxibustion was applied in the combination group, once a week. The 14-day treatment was taken as one course, and totally 2 courses with 2-day interval were required in the two groups. The clinical symptom score, the visual analogue scale (VAS) score and the Western Ontario and McMaster Universities arthritis index (WOMAC) score were observed before treatment, after treatment and in the follow-up of 12 weeks after treatment; the volume of suprapatellar bursa effusion was detected before and after treatment; the clinical efficacy was evaluated after treatment and in the follow-up in the two groups. RESULTS: <0.05). CONCLUSION: deficiency and cold congelation, its short-term effect and long-term effect are both superior to 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 | 0.000 | 0.000 |
| 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".