The effectiveness of Tai Chi for patients with mild cognitive impairment: a systematic review and meta−analysis
Bibliographic record
Abstract
Objective To explore the effectiveness of Tai Chi on cognitive function in patients with mild cognitive impairment (MCI). Methods According to the PRISMA guidelines, randomized controlled trial (RCT) literature on the efficacy of Tai Chi on MCI patients was searched in China National Knowledge Network (CNKI), China Biomedical Literature Database (CBM), Wanfang Data, China Scientific Journal Database (VIP), PubMed, Embase, Duxiu Database, Web of Science and Cochrane Library from their inception to April 2024. The risk of bias in each study was appraised using the Cochrane risk−of−bias tool using Revman 5.4. Random effect model or fixed effect model was used to compare the effects of Tai Chi and control conditions on baseline and post−intervention assessment of cognitive function. Meta−analysis was performed using Stata15.0 software. Results Nine studies fulfilled the inclusion criteria. Tai Chi significantly improved Montreal Cognitive Assessment (MoCA, SMD, 1.43, p < 0.00001), Delayed Recall Test (DRT, SMD, 0.90, p < 0.00001), verbal fluency test (VFT, SMD, 0.40, p < 0.00001), and Trail Making Test (TMT, SDM, −0.69, p < 0.00001) in MCI patients. Subgroup analyses showed that 24-forms Tai Chi was more effective than 8-forms Tai Chi in improving MoCA (SMD, 1.89, p < 0.00001) and 10-forms Tai Chi was more effective than 24-forms Tai Chi in improving DRT (SMD, 1.53, p < 0.00001). Conclusion Tai Chi improved cognitive function in MCI patients, and Tai Chi types might be the influence factor on Tai Chi improving the global cognitive function and memory function in MCI patients. Systematic review registration https://www.crd.york.ac.uk/prospero/ .
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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 | 0.014 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.022 | 0.033 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".