Transcranial direct current stimulation in Parkinson’s disease management
Bibliographic record
Abstract
Background. Transcranial direct current stimulation (tDCS) is discussed as a promising noninvasive neuromodulation method for alleviating symptoms of Parkinson’s disease (PD). However, evidence on the efficacy of tDCS in PD remains limited in our country. Aim. To evaluate the effect of a course of noninvasive anodal tDCS on the motor symptoms, cognitive function, and emotional state of patients with PD. Materials and methods. Twenty-nine patients with PD (Hoehn and Yahr stages I–III), aged 45–80 years, received anodal tDCS over the dorsolateral prefrontal cortex (DLPFC) at 2 mA for 20 minutes, once daily, 5 days per week for 2 weeks (10 sessions total). Pre- and post-treatment assessments included motor function (Unified Parkinson’s Disease Rating Scale, Part III [UPDRS-III]), cognition (Montreal Cognitive Assessment [MoCA], Frontal Assessment Battery [FAB], Trail Making Test Parts A and B [TMT-A/B]), and affective measures (Apathy Scale; State-Trait Anxiety Inventory-trait and state; Geriatric Depression Scale [GDS]). Results. After the tDCS course, there was a statistically significant improvement in cognitive measures (higher MoCA scores and shorter TMT-A/B completion times), a reduction in motor deficit (lower UPDRS-III scores), and a decrease in apathy and trait anxiety (p0,05). No significant changes were observed on the FAB, state anxiety, or GDS. No adverse events were reported during tDCS in PD. Conclusion. A course of tDCS appears safe and may improve cognitive, affective (apathy, anxiety), and motor symptoms in PD. Randomized, sham-controlled trials are warranted to confirm efficacy and to determine optimal stimulation parameters.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".