Deep Brain Stimulation at Variable Frequency to Improve Motor Outcomes in Parkinson's Disease
Bibliographic record
Abstract
INTRODUCTION: Deep brain stimulation (DBS) with high frequency (HFS) is a well-established therapy for Parkinson's disease (PD); however, low frequency DBS (LFS) may control axial symptoms including freezing of gait (FOG). We conducted a pilot safety and feasibility study to examine if a novel DBS paradigm of variable frequency stimulation (VFS) that combined HFS and LFS would capture a broader set of motor symptoms. METHODS: Four PD patients with bilateral STN DBS and FOG were enrolled. A UPDRS III and 10 m timed up and go (TUG) task were performed off medications-off DBS and then one hour after HFS and one hour after VFS programming. RESULTS: The UPDRS III motor score improved by additional 14% during VFS setting when compared to HFS. VFS also increased gait speed (mean change 45%) and reduced the number of freezing episodes (mean change 58%). CONCLUSIONS: VFS improves UPDRS and FOG in PD when compared to HFS.Copyright © 2018 International Parkinson and Movement Disorder Society.
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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".