Clinical Relevance Of Tai Chi On Pain, Stiffness, And Function In Knee Osteoarthritis: A Meta-analysis
Bibliographic record
Abstract
The clinical relevance of Tai Chi on pain, stiffness, and physical function in adults with knee osteoarthritis (KOA) has not been established. PURPOSE: To determine the clinical relevance of Tai Chi on pain, stiffness, and physical function in adults with KOA. METHODS: Eight randomized controlled trials representing 407 participants (216 Tai Chi, 191 control) in adults >18 years of age with KOA and included the assessment of pain, stiffness, and physical function using the Western Ontario and McMaster Universities Arthritis Index (WOMAC) were included. The inverse variance heterogeneity model (IVhet) was first used to pool standardized mean difference effect sizes (ES) for each outcome. Clinical relevance, i.e., number-needed-to treat (NNT) <10 and relative risk reduction (RRR) > 25% was calculated across assumed controlled risks (ACR) ranging from 0.01 to 0.99. RESULTS: Statistically significant improvements were found for pain (ES, -0.75, 95% CI, -0.99, -0.51; Q = 8.9, p = 0.26; I2 = 21%), stiffness (ES, -0.70, 95% CI, -0.95, -0.46; Q = 9.6, p = 0.21; I2 = 27%), and physical function (ES, -0.91, 95% CI, -1.12, -0.70; Q = 7.2, p = 0.40; I2 = 3%). The intersection of results for a NNT <10 and RRR >25% yielded high evidence and clinically relevant improvements across a wide range of ACR for pain (0.15 to 0.88), stiffness (0.15 to 0.87), and physical function (0.13 to 0.97). CONCLUSION: Tai Chi results in statistically significant as well as clinically important improvements in pain, stiffness, and physical function across a wide range of ACR in adults with KOA.
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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 | 0.019 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.053 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".