Effect of Acupuncture on Cognitive Function in Patients With Post‐Stroke Cognitive Impairment: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
AIMS AND OBJECTIVE: To investigate the impact of acupuncture on post-stroke cognitive impairment (PSCI). BACKGROUND: PSCI is a major barrier to stroke patients' rehabilitation, and acupuncture is one of the treatments. However, the benefit of acupuncture on PSCI is unclear. DESIGN: A meta-analysis and systematic review of randomized controlled trials (RCTs). METHODS: Up to February 1, 2024, databases in PubMed, MEDLINE, Scopus, Embase, Web of Science, Cochrane Library, China National Knowledge Infrastructure, VIP, and Wanfang Data were searched. The risk of bias was investigated using the Cochrane Handbook for systematic reviews of treatments. Random-effect and fix-effect models were used to report the effects. RESULTS: = 59%, p < 0.01). Compared to medicine group, the Loewenstein Occupational Therapy Cognitive Assessment (LOTCA) score exhibited a significant decrease and demonstrated improvement in the acupuncture group. Statistically significant outcomes were observed in the Barthel Index scores and P300 event-related potential (ERP). According to subgroup analysis, acupuncture was superior to conventional therapy for improving cognitive function in PSCI patients at 4 weeks after treatment. CONCLUSION: Acupuncture therapy has shown promise in ameliorating cognitive deficits and enhancing daily functional abilities in individuals diagnosed with PSCI. But future research should focus on the duration and implement large sample, high-quality RCTs. RELEVANCE TO CLINICAL PRACTICE: Clinical workers in practical clinical work can select appropriate acupoints according to the actual conditions of patients, as well as confirm the treatment course of PSCI patients, while paying attention to observing and evaluating the therapeutic efficacy of acupuncture, to improve the health outcomes of patients in a patient-centered way.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.028 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| 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".