The Effectiveness and Safety of Manual Acupuncture Therapy in Patients with Poststroke Cognitive Impairment: A Meta-analysis
Bibliographic record
Abstract
Background. Poststroke cognitive impairment (PSCI) is a common cause of disability among patients with stroke. Meanwhile, acupuncture has increasingly been used to improve motor and cognitive function for stroke patients. The aim of the present study was to summarize and evaluate the evidence on the effectiveness of acupuncture in treating PSCI. Methods. Eight databases (PubMed, The Cochrane Library, CNKI, WanFang Data, VIP, CBM, Medline, Embase databases) were searched from January 2010 to January 2020. Meta-analyses were conducted for the eligible randomized controlled trials (RCTs). Assessments were performed using Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), Barthel Index (BI), or modified Barthel Index (MBI). Results. A total of 657 relevant RCTs were identified, and 22 RCTs with 1856 patients were eventually included. Meta-analysis showed that acupuncture appeared to be effective for improving cognitive function as assessed by MMSE ( mean difference MD = 1.73 , 95% confidence interval (CI) (1.39, 2.06), P < 0.00001 ) and MoCA ( MD = 2.32 , 95% CI (1.92, 2.73), P < 0.00001 ). Furthermore, it also suggested that acupuncture could improve the activities of daily life (ADL) for PSCI patients as assessed by BI or MBI ( SMD = 0.97 , 95% CI (0.57, 1.38), P < 0.00001 ). Conclusions. Compared with nonacupuncture group, acupuncture group showed better effects in improving the scores of MMSE, MoCA, BI, and MBI. This meta-analysis provided positive evidence that acupuncture may be effective in improving cognitive function and activities of daily life for PSCI patients. Meanwhile, long retention time of acupuncture may improve cognitive function and activities of daily life, and twist technique may be an important factor that could influence cognitive function. However, further studies using large samples and a rigorous study design are needed to confirm the role of acupuncture in the treatment of PSCI.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.034 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".