Clinical efficacy evaluation of a traditional Miao technique of crossbow needle therapy in the treatment of knee osteoarthritis: a multi-center randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Knee osteoarthritis (KOA) seriously reduces quality of life and is a major threat to the health of the middle-aged and elderly. This study aimed to assess the efficacy of Miao crossbow needle therapy vs. acupuncture for KOA therapy. METHODS: This multicenter, randomized controlled trial was performed at three hospitals between April 2016 and December 2016. The patients were randomized to receive crossbow needle (CN) or acupuncture (AT). All treatments were completed within 46 days. Evaluation of treatment was conducted on the 46th, 62nd, and 77th days. The primary endpoint was change of Western Ontario and McMaster Osteoarthritis Index (WOMAC) score on the 46th day. The secondary endpoints included WOMAC score, the Lysholm knee score, the Japanese Orthopedic Association (JOA) knee score, visual analog scale (VAS), and the MOS 36-item short-form health survey (SF-36), on the 46th, 62nd, and 77th day. RESULTS: Finally, data of 301 participants were analyzed for the efficacy of treatment. Compared with AT, there was a larger change of WOMAC score in the CN group after treatment [- 25.0 (95% CI - 27.0, - 23.0) vs. - 18.8 (95% CI - 20.8, - 16.9), P < 0.001]. In the CN group, the WOMAC score was lower at all three time points (P = 0.008, P = 0.003, P < 0.001 respectively), while the Lysholm knee score (P = 0.03) and JOA score (P = 0.013) were higher and the VAS score (P = 0.011) was lower on the 77th day. CONCLUSION: Both Miao crossbow needle therapy and acupuncture reduced the WOMAC score. Miao crossbow needle therapy can be an alternative method for treating patients with knee osteoarthritis. TRIAL REGISTRATION: ChiCTR, ChiCTR-INR-16008032. Registered on 12 March 2016.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".