P.089 Ultra-high frequency deep brain stimulation at 10,000 Hz improves motor function
Bibliographic record
Abstract
Background: Stimulation frequency has been considered a crucial determinant of efficacy in deep brain stimulation (DBS). DBS at frequencies over 250Hz is not currently employed and consensus in the field suggests that higher frequencies are not clinically effective. With the recent demonstration of clinically effective ultra-high frequency (UHF) spinal cord stimulation at 10kHz we tested whether UHF stimulation could also be clinically useful in movement disorder patients with DBS. Methods: We studied the effects of conventional (130Hz) and UHF stimulation in five patients with Parkinson’s disease (PD) with STN DBS and in one patient with essential tremor (ET) with VIM DBS. We compared the clinical benefit and adverse effects of stimulation at various amplitudes either intraoperatively or postoperatively with the electrodes externalized. Results: Motor performance improved in all six patients with UHF DBS. 10kHz stimulation at amplitudes ≥3.0mA appeared to be as effective as 130Hz in improving motor symptoms (46.2% vs 53.5% motor score reduction, p=0.110, N=90 trials). Interestingly, 10kHz stimulation resulted in fewer stimulation-induced paresthesiae and speech adverse effects than 130Hz stimulation. Conclusions: Our results indicate that DBS at 10kHz produces clinical benefits while possibly reducing stimulation-induced adverse effects in patients with movement disorders.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.015 | 0.001 |
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".