Efficacy evaluation of acupotomy combined with platelet-rich plasma in the treatment of early and middle osteoarthritis.
Bibliographic record
Abstract
OBJECTIVE: To investigate the efficacy of traditional Chinese medicine acupotomy combined with platelet-rich plasma (PRP) in the treatment of early and middle osteoarthritis. METHODS: Eighty cases of early and middle knee joint pain patients admitted in our hospital were selected in this retrospective study. They were divided into the control group and observation group according to treatment methods, with 40 cases in each group. The control group was treated with PRP, and the observation group was treated with acupotomy + PRP. Clinical response rate, visual analogue scale (VAS) pain score, Lequesne score, Western Ontario and McMaster Universities Osteoarthritis (WOMAC) index and SF-36 quality of life score were compared between the two groups. RESULTS: The total clinical response rate in the observation group was higher than that in control group (P<0.01). VAS pain score, knee joint WOMAC index and Lequesne score in the two groups after treatment were lower than those before treatment, and those in the observation group were lower than those in the control group (all P<0.05). SF-36 quality of life score was significantly higher in the observation group than in the control group (all P<0.001). CONCLUSION: Acupotomy combined with PRP in the treatment of early and middle osteoarthritis can relieve pain and improve joint function, which is worthy of clinical promotion.
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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) | 0.000 | 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".