Effects of traditional Chinese exercises or their integration with medical treatments on cognitive impairment: a network meta-analysis based on randomized controlled trials
Bibliographic record
Abstract
Objective: This study aims to summarize and critically evaluate the effects of traditional Chinese exercises, both in isolation and in combination with medical treatments, on cognitive impairment. Methods: A systematic search of academic databases, including PubMed, Embase, Web of Science, Cochrane Library, CNKI, Wanfang, and VIP, was conducted to identify the randomized controlled trials (RCTs) that evaluated traditional Chinese exercises and their integration with medical treatments for addressing cognitive impairment. Study quality was assessed using the Cochrane Handbook's Risk of Bias tool. A total of 24 RCTs involving 1,808 participants were included. The primary outcome measures were the Montreal Cognitive Assessment (MOCA) and the Mini-Mental State Examination (MMSE). Subgroup analyses were performed to compare the intervention effects. Results: The network meta-analysis revealed that acupuncture combined with Tai Chi (Aandtaiji) showed the most significant improvement in MOCA scores, followed by Qigong. Tai Chi soft ball exercise (Taijiball) demonstrated the greatest improvement in MMSE scores. Conclusion: The combination of traditional Chinese exercises with medical treatment is more effective in improving MOCA scores, while traditional exercises alone yield better results to enhance MMSE scores. The extended practice of Tai Chi and Qigong enhances cognitive function in patients with cognitive impairment.
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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 | 0.023 | 0.054 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.023 | 0.034 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".