Acupuncture for cognitive impairment after stroke: A systematic review and meta-analysis
Bibliographic record
Abstract
Objective: Acupuncture as an alternative therapy for post-stroke cognitive impairment (PSCI) has emerged as a research focus. The inclusion of additional external treatments in many previous studies prevents a clear, direct assessment of acupuncture's impact on PSCI. In order to prevent patients from developing hypersensitivity to other treatments and misinterpreting acupuncture's true therapeutic value, this study establish stricter intervention criteria and exclude therapies beyond acupuncture. The review aimed to offering a clearer evaluation of acupuncture's efficacy and safety in PSCI treatment. Methods: This research involved a comprehensive search for randomized controlled trials (RCTs) across eight databases, adhering to the Cochrane Systematic Reviewer's Handbook 5.1.0 for risk-of-bias and quality assessments. A meta-analysis was conducted using RevMan 5.3 software. Results: The inclusion of 18 publications, totaling 1361 patients, was achieved. The meta-analysis demonstrated a significantly higher overall efficacy of acupuncture for PSCI compared to controls (OR = 4.06, 95 % CI 2.86-5.76, Z = 7.82). Notable statistical differences were observed in the Montreal Cognitive Assessment scores (MD = 2.32, 95 % CI 1.68-2.97, Z = 7.10) and the Mini-Mental State Examination scores (MD = 2.02, 95 % CI 1.06-2.98, Z = 4.13) between the groups. Improvements in the Barthel Index scores were noted for the experimental group (MD = 5.70, 95 % CI 4.68-6.72, Z = 10.92). Conclusion: Integrating acupuncture with Western medications offers significant benefits for treating PSCI over Western medications alone. However, the long-term efficacy of acupuncture in PSCI treatment and its potential in reducing recurrence rates remain undetermined. Further high-standard RCTs are essential to explore acupuncture's effectiveness in PSCI treatment more thoroughly.
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.033 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".