Cognitive Training and Transcranial Direct Current Stimulation for Mild Cognitive Impairment in Parkinson’s Disease: A Randomized Controlled Trial
Bibliographic record
Abstract
This study examined whether standard cognitive training, tailored cognitive training, transcranial direct current stimulation (tDCS), standard cognitive training + tDCS, or tailored cognitive training + tDCS improved cognitive function and functional outcomes in participants with PD and mild cognitive impairment (PD-MCI). Forty-two participants with PD-MCI were randomized to one of six groups: (1) standard cognitive training, (2) tailored cognitive training, (3) tDCS, (4) standard cognitive training + tDCS, (5) tailored cognitive training + tDCS, or (6) a control group. Interventions lasted 4 weeks, with cognitive and functional outcomes measured at baseline, post-intervention, and follow-up. The trial was registered with the Australian New Zealand Clinical Trials Registry (ANZCTR: 12614001039673 ). While controlling for moderator variables, Generalized Linear Mixed Models (GLMMs) showed that when compared to the control group, the intervention groups demonstrated variable statistically significant improvements across executive function, attention/working memory, memory, language, activities of daily living (ADL), and quality of life (QOL; Hedge’s g range = 0.01 to 1.75). More outcomes improved for the groups that received standard or tailored cognitive training combined with tDCS. Participants with PD-MCI receiving cognitive training (standard or tailored) or tDCS demonstrated significant improvements on cognitive and functional outcomes, and combining these interventions provided greater therapeutic effects.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".