Efficacy and safety of acupuncture therapy for neuropsychiatric symptoms among patients with Parkinson's disease: A systematic review and meta-analysis
Bibliographic record
Abstract
Objective To systematically evaluate the efficacy and safety of acupuncture therapy for neuropsychiatric symptoms in patients with Parkinson's disease. Methods We searched eight databases from their inception until 14 April 2024, including PubMed, Cochrane Library, Embase, Web of Science, SinoMed, China National Knowledge Infrastructure, China Science and Technology Periodical Database, and Wanfang Database. The search aimed to find randomized controlled trials assessing the effectiveness of acupuncture for neuropsychiatric symptoms in patients with Parkinson's disease. Literature screening and data extraction were performed independently by the authors. Meta-analysis was conducted using RevMan V.5.3 software, and Stata 17.0 software was used for detecting publication bias and performing sensitivity analysis. Results Twenty-eight studies, involving 2148 participants, met the inclusion criteria. The meta-analysis revealed that acupuncture therapy improved depression-related scale scores (standardized mean difference (SMD) = –0.70, 95%CI [–0.98, −0.42], p < 0.00001), anxiety-related scale scores (SMD = –0.78, 95% CI [–1.43, −0.14], p = 0.02), Montreal Cognitive Assessment scores (weighted mean difference (WMD) = 2.74, 95% CI [2.43, 3.05], p < 0.00001), Mini Mental State Examination scores (WMD = 2.36, 95% CI [0.78, 3.94], p = 0.003), Yale-Brown Obsessive Compulsive Scale scores, and Parkinson's Disease Questionnaire-39 scores (WMD = –2.66, 95% CI [–4.83, −0.49], p = 0.02) compared to controls. Conclusion This review supports the application of acupuncture to reduce the severity of neuropsychiatric symptoms including depression, anxiety, and impulse control disorders, and to improve cognition and quality of life in patients with Parkinson's disease. The adverse effects associated with acupuncture, either alone or as adjunctive therapy, were relatively minor.
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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 | 0.015 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.024 | 0.033 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".