[Point-pricking method with fire needle for knee osteoarthritis: a randomized controlled trial].
Bibliographic record
Abstract
OBJECTIVE: To observe the effects of point-pricking method with fire needle on the symptoms of knee joint and physio-psychological health in the patients with knee osteoarthritis (KOA). METHODS: Sixty six patients with KOA were randomly divided into a fire needling group (33 cases) and a filiform needling group (33 cases). The patients received the point-pricking method with fire needle in the fire needle group while the conventional acupuncture with filiform needle was provided in the filiform needling group. The basic health management was performed in both groups. The acupoints included bilateral Liangqiu (ST34), Xuehai (SP10), Dubi (ST35), Neixiyan (EX-LE4), Yanglingquan (GB34) and Zusanli (ST36) as well as Ashi points. The treatment was conducted twice a week for 6 weeks consecutively. Before and after treatment, the scores of Western Ontario and McMaster University Osteoarthritis Index (WOMAC), traditional Chinese medicine (TCM) symptoms and visual analogue score (VAS), the numbers of affected areas of knee joint pain and the scores of 12-item short-form health survey (SF-12) were assessed and the incidence of adverse effects was recorded. RESULTS: <0.05) in the fire needling group after treatment. None adverse effects were found in either group. CONCLUSION: The point-pricking method with fire needle is safe and effective when compared with conventional acupuncture with filiform needle. In the aspects of improving knee joint function, relieving joint pain and advancing the quality of life, the point-pricking method with fire needle is superior to the conventional acupuncture with filiform needle.
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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) | 0.004 | 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) | 0.008 | 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".