Replication Data for: Individualising galvanic vestibular stimulation further improves visuomotor performance in Parkinson’s disease
Bibliographic record
Abstract
This dataset contains data from a study investigating the effects of galvanic vestibular stimulation on visuomotor performance inpatients with Parkinson's disease. Abstract Impaired motor function is a defining characteristic of Parkinson’s disease (PD). Galvanic vestibular stimulation (GVS) has been proposed as a potential non-invasive intervention to enhance motor performance; however, its efficacy depends on both stimulation parameters and electrode configuration. In this study, we examined the effects of two-pole and three-pole GVS configurations, utilising different stimulation parameters, on motor performance in individuals with PD. Twelve participants with PD were administered eight distinct subthreshold amplitude-modulated GVS stimuli, along with sham stimulation, while performing a visuomotor target tracking task. Analysis of tracking error demonstrated substantial inter-individual variability in response to different stimuli and electrode configurations. While the three-pole configuration yielded superior motor performance in some cases, the two-pole configuration was more effective in others. The most effective overall stimulus across all subjects, characterised by an envelope frequency of 30 Hz and a carrier frequency of 110 Hz, improved motor performance by 25% relative to the sham stimulus. Moreover, tailoring the stimulation parameters to the individual further enhanced performance by an additional 24%. These findings suggest that GVS can yield significant motor improvements in individuals with PD. Furthermore, individualised optimisation of stimulation parameters, including the selection of the appropriate electrode configuration, may further enhance therapeutic efficacy.
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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 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.041 | 0.008 |
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".