Effects of Tai Chi exercise on improving walking function and posture control in elderly patients with knee osteoarthritis
Bibliographic record
Abstract
OBJECTIVE: It remains unclear whether Tai Chi is effective for walking function and posture control improvements in aged populations with knee osteoarthritis. The aim of this study was to systematically evaluate the effects of Tai Chi on improving walking function and posture control in elderly patients with knee osteoarthritis by updating the latest trial evidence. METHODS: Web of Science, PubMed/Medline, Embase, Scopus, PEDro, and Cochrane library were searched up to October 1, 2020 to identify RCTs evaluating Tai Chi for improving walking function and posture control in older adults with knee osteoarthritis. The primary outcomes were walking function and posture control. Meta-analysis was performed with RevMan Version 5.3 software. RESULTS: A total of 603 participants with knee osteoarthritis in the 11 trials were included. The results of meta-analysis showed that: The Tai Chi group was associated with better performance in 6-minute walk test (6 MWT), time up and go test (TUG) and "Western Ontario and McMaster Universities (WOMAC) Osteoarthritis Index" Physical Function Score than the control group ([MD: 46.67, 95% CI 36.91-56.43, P < .001]), ([MD: -0.89, 95% CI -1.16 to -0.61, P < .001]), ([MD: -11.28, 95% CI -13.33 to -9.24, P < .001]). CONCLUSION: This meta-analysis provided evidence from 11 RCTs that Tai Chi could be an excellent physical training strategy for improving walking function and posture control in older adults with knee osteoarthritis. Assuming that Tai Chi is at least effective and safe in most areas, it can be used as an adjuvant and reliable physical training strategy for walking function upgrading and balance control improvements for older patients with knee osteoarthritis.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".