Effect of traditional Chinese exercises on knee osteoarthritis: A network meta-analysis
Bibliographic record
Abstract
BACKGROUND: Osteoarthritis is the third leading risk factor for disability in older adults. OBJECTIVE: To compare the efficacy of different traditional Chinese exercises on knee osteoarthritis by network meta-analysis, and to provide a reference basis for patients to choose the best method. METHODS: Seven databases, including Pubmed, Embase, The Cochrane Library, Web of Science, China National Knowledge Infrastructure, WanFang, and China Science and Technology Journal Database were searched for literature on traditional Chinese exercise to improve the symptoms of patients with knee osteoarthritis. The search period was from inception of the database until February 14, 2024. Literature screening and data extraction were carried out independently by 2 investigators, and the quality of the included studies was evaluated using the Cochrane Risk of Bias 2.0 assessment tool. R4.2.3 and Stata 15.0 were used for analysis. RESULTS: Forty-two studies involving 2843 patients were ultimately included, encompassing 4 kinds of traditional Chinese exercise. The surface under the cumulative ranking curve (SUCRA) showed that Baduanjin was the best traditional Chinese exercises for Western Ontario and McMaster University Osteoarthritis Index scores including pain score (SUCRA = 0.85), stiffness score (SUCRA = 0.87), physical function score (SUCRA = 0.88) and overall score (SUCRA = 0.83). For Visual Analog Scale pain score, the most effective traditional Chinese exercise was Tai Chi (SUCRA = 0.93). CONCLUSION: The efficacy of Tai Chi, Baduanjin, Yijinjing, and Wuqinxi on knee osteoarthritis patients is superior to that of usual care. Baduanjin had the best effect in improving stiffness, physical function and overall score, and both Baduanjin and Tai Chi were the best options for improving pain.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".