Does acupuncture have advantages in the rehabilitation of vascular mild cognitive impairment? A systematic review and meta-analysis
Bibliographic record
Abstract
Background: Vascular mild cognitive impairment (VMCI) is a common impairment caused by vascular factors. VMCI often occurs after stroke, and it is the main clinical manifestation of long-term disability. Many patients are treated with acupuncture in combination with other therapies. However, evidence regarding the effectiveness of this treatment regimen is lacking. Aims: This meta-analysis aimed to evaluate the efficacy of acupuncture therapy for treating VMCI. Methods: This systematic review was conducted in accordance with the preferred reporting and meta-analysis guidelines. The CNKI, Wanfang, VIP, CBM, Cochrane Library, PubMed and Embase databases were searched from inception to August 20, 2022. After two researchers independently screened the literature, they extracted the data and evaluated the risk of bias in the included studies. Revman 5.3 software was used for the meta-analysis. Summary of review: Thirty-two randomized controlled trials (RCTs) were included. The overall effective rate of acupuncture for treating VMCI was 3.06, 95% CI [2.39, 3.91], (P < 0.05). Montreal Cognitive Assessment (MoCA), Mini-Mental State Examination (MMSE), Barthel Index and Activities of Daily Living (ADLs) scores significantly differed between the treatment and control groups, with weighted mean differences (WMDs) [95% CI] (P value) of 1.97 [1.44, 2.49] (P < 0.05), 2.02 [1.50, 2.54] (P < 0.05), 5.54 [3.81, 7.28] (P < 0.05), and 3.43 [2.53, 4.33] (P < 0.05), respectively. The overall effective rate of electroacupuncture (EA) for treating VMCI was better than that of the control group (RR = 2.25, 95% CI, [1.13, 4.50], P < 0.05). MoCA, MMSE, Barthel index and ADL scores differed significantly between the treatment and control groups, with WMDs [95% CI] (P value) of 1.79 [1.20, 2.38] (P < 0.05), 1.45 [0.87, 2.03] (P < 0.05), 5.78 [2.38, 9.18] (P < 0.05), and 3.15 [2.15, 4.15] (P < 0.05), respectively. Acupuncture alone and combined with drug therapy were thus superior to drug therapy alone for improving cognitive function. EA also has potential advantages. Conclusions: Acupuncture combined with another therapy is better than other therapies alone, such as simple drug therapy, for treating VMCI. However, variations in study duration (4-12 weeks) limit us from drawing any definitive conclusions about long-term effects. Therefore, more RCTs with rigorous designs and reasonable treatment and follow-up durations are needed.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.013 | 0.005 |
| Bibliometrics | 0.001 | 0.002 |
| 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".