Long-term symptom recurrence and functional outcomes with Chinese ointment massage, Tuina, and combined acupuncture for knee osteoarthritis: A 24-month multicenter real-world study in China
Bibliographic record
Abstract
PURPOSE: Compare long-term efficacy and recurrence of Chinese ointment(Co) massage, Tuina, and combined acupuncture for knee osteoarthritis (KOA). METHOD: A multicenter, prospective, observational real-world study was conducted in multiple cities in China. 2143 KOA patients (83.76 % female) were prospectively assigned by preference to Co (n = 312), Tuina (n = 403), Co+acupuncture (n = 237), or Tuina+acupuncture (n = 1362) groups. Treatments involved 5-10 sessions. The Visual Analog Scale (VAS) assessed the average degree of knee joint pain, while the Western Ontario and McMaster University Osteoarthritis Index (WOMAC) and Lequesne Index evaluated knee joint dysfunction in terms of functionality and severity. The 2 years recurrence rate of KOA was also calculated. RESULTS: All groups showed significant post-treatment WOMAC, VAS, and Lequesne score reductions. The Co group had the lowest VAS scores at 2-10 months. Recurrence rates (lowest to highest) were Co group, Tuina group, Tuina+acupuncture group, Co+acupuncture group. The overall maximum recurrence rate was 33.97 % at 2 years, stabilizing after 18 months. KL grade II patients had the best long-term outcomes (recurrence 16.16 %-30.85 % over 2-24 months). No serious adverse events occurred. CONCLUSIONS: Traditional Chinese medicine (TCM) conservative treatments (Co therapy, Tuina, and combined acupuncture) demonstrated safety for KOA management. Patients with lower KL grades (0-III) derived greater clinical benefit from longer courses (≥10 sessions), with effects sustained for up to 2 years. Co therapy or acupuncture-integrated regimens provided optimal early improvement (within the first 5 sessions) for pain and mobility. Intensive combination protocols offered no additional cumulative benefit for patients with mild symptoms.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".