Clinical observation of sinew-regulating and bone-setting manipulation combined with Xiaoyu Jiegu Powder in treating knee osteoarthritis
Bibliographic record
Abstract
Objective: To evaluate the clinical efficacy of sinew-regulating and bone-setting \nmanipulation(SRBSM) combined with Xiaoyu Jiegu Powder ( XYJGP) in treating knee \nosteoarthritis(KOA). Methods: Sixty patients were randomly divided into treatment group (30 \ncases) and control group(30 cases). The treatment group was treated with SRBSM combined \nwith XYJGP and the control group was given intermediate frequency electrotherapy and \nXYJGP. Visual Analogue Scale (VAS). The Western Ontario and McMaster Universities \nOsteoarthritis Index(WOMAC), Flexion range of motion of the knee joint, and surface \nelectromyography（sEMG） were used to evaluate and compare the patients' knee function \nbefore and after treatment. The efficacy was observed with diagnosis and efficacy standards \nof TCM. Results: After treatment, compared with the same group before treatment, muscle \nfatigue and muscle tension decreased, pain was reduced, range of motion of the knee joint \nwas increased, and motor function was improved (P<0.05). The improvement degree of the \ntreatment group was better than that of the control group (P<0.05). The total effective rate of \nthe treatment group was 96.6% (29/30), higher than that of the control group (80.0%), P<0.05. \nConclusion: SRBSM combined with XYJGP can effectively reduce the pain of KOA patients, \nimprove the range of motion of the joints, and improve the quality of life of the patients, and \nits curative effect is better than that of intermediate frequency electrotherapy combined with \nXYJGP.
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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) | 0.001 | 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".