Effectiveness and safety of Governor vessel acupuncture therapy for post-stroke cognitive impairment: A meta-analysis of randomized controlled trials
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to evaluate the effectiveness of Governor vessel acupuncture (GV Ac) in treating post-stroke cognitive impairment (PSCI). METHODS: There was a total of seven databases examined. Four English databases (Cochrane Library, PubMed, Embase, and Medline) and three Chinese databases (Chinese National Knowledge Infrastructure (CNKI), Chinese Science and Technology Periodical Databases (VIP), and Wan Fang Database) contain all randomized controlled trials (RCTs) comparing Governor vessel acupuncture to other treatments or none acupuncture for PSCI. The exact dates for the search period are from January 1, 2000, to January 1, 2023.Two researchers independently reviewed the literature, gathered RCT data, and performed statistical analysis. All data were analyzed using Review Manager software (Rev Man) 5.3. RESULTS: This meta-analysis includes a total of 39 trials with 2044 patients. There were 1022 participants in each of the test and control groups. Following 12-120 days of acupuncture treatment, a meta-analysis revealed that the treatment groups (GV Ac combined with conventional treatment groups) significantly increased their scores on the Curative ratio (OR = 3.00, 95 %CI = 2.37-3.79, P = 0.98, I² = 0 %), Montreal Cognitive Assessment (MoCA)(MD = 1.82, 95 %CI = 1.60-2.03, P = 0.11, I² = 25 %), Mini-Mental State Examination (MMSE)(MD = 2.18, 95 %CI = 1.64-2.72, P<0.005, I² = 92 %), and Activity of Daily Living (ADL)(MD = 5.99, 95 %CI = 5.33-6.64, P = 0.19, I² = 26 %). CONCLUSION: The results suggested that acupuncture on points of the Governor vessel enhanced cognitive function in stroke survivors.
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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 | 0.111 | 0.107 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.107 | 0.053 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".