Effects Of Tai Chi Exercise For Symptoms Of Knee Osteoarthritis: A Systematic Review And Meta-analysis
Bibliographic record
Abstract
PURPOSE: To systematically review and meta-analyze the effects of Tai Chi Exercise for symptoms of knee osteoarthritis (KOA). METHODS: We performed systematic searches in five electronic databases: PubMed, Web of Science, Cochrane Library, EBSCO and EMBASE from the time of their inception to August 2021. All eligible Randomized Controlled Trials (RCTs) were included in which Tai Chi was utilized to treat KOA compared to a control group. Three reviewers independently extracted the data and evaluated the risk of bias following the Cochrane Risk of Bias Tool for RCTs. The symptom of KOA evaluated by the Western Ontario and McMaster Universities Arthritis Index (WOMAC) was regarded as the primary outcomes in this study. Outcomes measures included pain, joint stiffness and physical function. Each outcome measure was pooled by a standardized mean difference (SMD) with 95% confidence intervals (CI). A meta-analysis was applied with a random effect model for the collected data to calculate the summary SMD with 95% CI based on different statistical heterogeneity. RESULTS: A total of 11 RCTs with 709 patients with KOA met the established inclusion criteria. The systematic review illustrated the efficacy of Tai Chi in treating and managing KOA. Patients' outcomes practising Tai Chi were improved significantly. Compared with a control group, the synthesized data of Tai Chi showed a significant reduction in WOMAC pain score (SMD = −0.64; 95% CI: −0.90 to −0.36; p < 0.001), stiffness score (SMD = −0.52; 95% CI: −0.84 to −0.21; p = 0.001), and physical function score (SMD = −0.77; 95% CI: −1.05 to −0.48; p < 0.001). No adverse events associated with Tai Chi were reported. CONCLUSIONS: Our study suggested that Tai Chi may effectively alleviate pain, relieve stiffness and improve the patients' physical function with KOA. Tai Chi was beneficial for alleviating the OA of patients and could be used as a rehabilitation exercise.
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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 | 0.044 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.027 | 0.035 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".