Systematic review and meta-analysis of acupuncture in the treatment of cognitive impairment after stroke
Bibliographic record
Abstract
BACKGROUND: We aim to make a systematic evaluation of the clinical efficacy of acupuncture in the treatment of cognitive impairment after stroke, to provide evidence-based medical evidence for clinical practice. METHODS: We searched all the randomized controlled trials of China National Knowledge Infrastructure, Wan fang data knowledge service platform, VIP Chinese periodical service platform full-text Journal Database, Chinese Biomedical Literature Database, Cochrane Library Database, and PubMed Database about acupuncture treatment of post-stroke cognitive impairment (PSCI). Two researchers independently screened the literature and extracted the data according to the inclusion and exclusion criteria. The bias risk assessment manual of Cochrane collaboration Network was used to evaluate the bias risk, and all data were analyzed by Stata16.0. RESULTS: Fourteen articles were included, with a total of 2402 patients. Meta-analysis showed that acupuncture combined with routine therapy could significantly reduce the score of cognitive impairment symptoms compared with the control group. The mini-mental state examination scale (MMSE) score (weighted mean difference [WMD] = 3.23, 95% confidence interval [CI]: 1.89-4.56, P < .01), Montreal cognitive assessment scale (MoCA) score (WMD = 3.41, 95% CI: 0.93-5.89, P < .01), Barthel index of activities of daily living (MBI) score (WMD = 4.59, 95% CI: 1.43-7.75, P < .01), and Lowenstein assessment scale (LOTCA) score (WMD = 8.60, 95% CI: 6.32-10.89, P = .00) were significantly improved in the patients receiving group acupuncture combined with routine therapy. CONCLUSION: Acupuncture combined with routine therapy seems to be more effective than conventional therapy alone in the treatment of PSCI. However, the differences between different acupuncture types need to be clarified in more high-quality randomized controlled trials.
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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 | 0.024 | 0.058 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.024 | 0.028 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| 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".