Impact of Tai Chi on Physical and Mental Well-being in Individuals With Knee Osteoarthritis
Bibliographic record
Abstract
OBJECTIVE: The aim was to examine the potential impact of Tai Chi on the enhancement of both physical and mental well-being in individuals with knee osteoarthritis. DESIGN: In this study, a search was conducted across the databases of PubMed, Cochrane Library, and Embase. The keywords "Tai Chi" and "knee osteoarthritis" were employed. RESULTS: Seventeen randomized controlled trials comprising 980 participants were included. The results indicated that Tai Chi was significantly associated with improvements in various measures, including the Western Ontario and McMaster Universities Arthritis Index (WOMAC) pain score (weighted mean difference -1.87), WOMAC stiffness score (weighted mean difference -0.62), WOMAC physical function score (weighted mean difference -10.33), Short Form Health Survey physical component summary score (weighted mean difference 3.17), and Short Form Health Survey mental component summary score (weighted mean difference 2.31). Furthermore, Tai Chi exercise demonstrated superior performance in the Timed Up and Go test, while no significant difference was observed in the 6-Min Walk Test (weighted mean difference 10.43). No serious adverse events were reported. CONCLUSIONS: The results of this study suggest that Tai Chi may have a significant effect on reducing pain, joint stiffness, and improving physical function in individuals with knee osteoarthritis, as measured by the WOMAC scale. Furthermore, Tai Chi shows promise in enhancing both the physical and mental aspects of quality of life, as well as improving performance in the timed up and go test.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".