Transcranial Direct Current Stimulation in Motor Therapy for Patients With Parkinson’s Disease: A Systematic Review
Bibliographic record
Abstract
Parkinson’s disease (PD) is a neurodegenerative disorder that leads to motor and non-motor impairments, including tremors, bradykinesia, and rigidity. Current treatments, such as pharmacological therapies and deep brain stimulation, have efficacy and side effects limitations. Transcranial direct current stimulation (tDCS) is a non-invasive technique that may offer a complementary approach to managing PD motor symptoms. This review aims to evaluate the efficacy of tDCS in improving motor outcomes in PD patients. This systematic review followed PRISMA guidelines and was registered on the PROSPERO platform (CRD4202457096). We included randomized controlled trials (RCTs) that assessed tDCS in PD patients with a control group. Studies were identified through PubMed, Embase, and Cochrane Library searches up to January 2025. Data on participant demographics, interventions, outcomes, and results were extracted and analyzed. Risk of bias was assessed using the Cochrane RoB 2 tool. Five studies involving 176 participants (mean age 67.13 years) were included. Results indicated that tDCS improved motor outcomes, including gait, posture, and balance. Notably, it demonstrated enhanced dual-task gait speed and improvements in gait cadence. Also, it was reported that there were reductions in freezing of gait severity, while improvements in trunk stability and postural control. Despite promising findings, heterogeneity across studies was observed, particularly regarding stimulation parameters and outcome measures. This review provides evidence that tDCS can improve motor symptoms in PD, particularly gait and posture. However, variability in stimulation protocols and small sample sizes limit generalizability. Larger, standardized trials are needed to confirm tDCS’s efficacy, optimize treatment parameters, and assess long-term effects. tDCS shows potential as an adjunctive therapy for managing motor symptoms in PD patients.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".