Tai Chi is effective in treating knee osteoarthritis: A randomized controlled trial
Bibliographic record
Abstract
OBJECTIVE: To evaluate the effectiveness of Tai Chi in the treatment of knee osteoarthritis (OA) symptoms. METHODS: We conducted a prospective, single-blind, randomized controlled trial of 40 individuals with symptomatic tibiofemoral OA. Patients were randomly assigned to 60 minutes of Tai Chi (10 modified forms from classic Yang style) or attention control (wellness education and stretching) twice weekly for 12 weeks. The primary outcome was the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain score at 12 weeks. Secondary outcomes included WOMAC function, patient and physician global assessments, timed chair stand, depression index, self-efficacy scale, and quality of life. We repeated these assessments at 24 and 48 weeks. Analyses were compared by intent-to-treat principles. RESULTS: The 40 patients had a mean age of 65 years and a mean body mass index of 30.0 kg/m(2). Compared with the controls, patients assigned to Tai Chi exhibited significantly greater improvement in WOMAC pain (mean difference at 12 weeks -118.80 mm [95% confidence interval (95% CI) -183.66, -53.94; P = 0.0005]), WOMAC physical function (-324.60 mm [95% CI -513.98, -135.22; P = 0.001]), patient global visual analog scale (VAS; -2.15 cm [95% CI -3.82, -0.49; P = 0.01]), physician global VAS (-1.71 cm [95% CI -2.75, -0.66; P = 0.002]), chair stand time (-10.88 seconds [95% CI -15.91, -5.84; P = 0.00005]), Center for Epidemiologic Studies Depression Scale (-6.70 [95% CI -11.63, -1.77; P = 0.009]), self-efficacy score (0.71 [95% CI 0.03, 1.39; P = 0.04]), and Short Form 36 physical component summary (7.43 [95% CI 2.50, 12.36; P = 0.004]). No severe adverse events were observed. CONCLUSION: Tai Chi reduces pain and improves physical function, self-efficacy, depression, and health-related quality of life for knee OA.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".