The effectiveness and safety of acupuncture for the treatment of cognitive impairment from Parkinson disease: A meta-analysis and systematic review
Bibliographic record
Abstract
BACKGROUND: This systematic review and meta-analysis examined the efficacy and safety of acupuncture for treating patients with cognitive impairment from Parkinson disease (PDCI). METHODS: We searched the China National Knowledge Infrastructure, Wanfang (WF), Weipu (VIP), China Biology Medicine, PubMed, Embase, Cochrane Library, Web of Science and Clinical Trials electronic databases from database inception to January 2024 to identify randomized controlled trials that examined the use of acupuncture to treat PDCI. Studies published in Chinese or English were considered eligible. Two independent reviewers performed the literature search. Data extracted from the included studies were analyzed via RevMan 5.4 software for Meta. The mean effect sizes and 95% confidence intervals were calculated. RESULTS: This meta-analysis ultimately included 9 articles involving a total of 629 patients. The outcome measures included the mini-mental state examination, the montreal cognitive assessment (MoCA), and the overall effective rate. The meta-analysis revealed that there were significant differences in all 3 outcomes between the experimental and control groups. CONCLUSIONS: Acupuncture can be used as an effective treatment for PDCI and is significantly superior to conventional treatments. However, considering the low methodological quality of the included studies, results of this meta-analysis should be interpreted with caution. In addition, due to the inconsistency observed in this study, more clinical trials are needed for further investigation.
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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 | 0.018 | 0.044 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.031 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| 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".