Transcranial Direct Current Stimulation for Parkinson's Disease: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Parkinson's disease is a common neurodegenerative disorder with motor and non-motor symptoms. Recently, as adjuvant therapy, transcranial direct current stimulation (tDCS) has been shown to improve the motor and non-motor function of patients with Parkinson's disease (PD). This systematic review aimed to evaluate the existing evidence for the efficacy of tDCS for PD. We included English databases (PubMed, the Cochrane Library, Embase, and Web of Science) and Chinese databases [Wanfang database, China National Knowledge Infrastructure (CNKI), China Science and Technology Journal Database (VIP), and China Biology Medicine (CBM)] without restricting the year of publication. Twenty-one tDCS studies, with a total of 736 participants, were included in the analysis. Two independent researchers extracted the data and characteristics of each study. There was a significant pooled effect size (−1.29; 95% CI = −1.60, −0.98; p < 0.00001; I 2 = 0%) in the Unified PD Rating Scale (UPDRS) I and the Montreal cognitive assessment (SMD = 0.87, 95% CI = 0.50 to 1.24; p < 0.00001; I 2 = 0%). The poor effect size was observed in the UPDRS III scores (SMD = −0.13; 95% CI = −0.64, 0.38; p = 0.61; I 2 = 77%), and similar results were observed for the timed up and go (TUG) test, Berg balance scale, and gait assessment. The results of this meta-analysis showed that there was insufficient evidence that tDCS improves the motor function of patients with PD. However, tDCS seemed to improve their cognitive performance. Further multicenter research with a larger sample size is needed. In addition, future research should focus on determining the tDCS parameters that are most beneficial to the functional recovery of patients with PD.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.028 |
| Bibliometrics | 0.006 | 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) | 0.005 | 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".