The Impact of Burr Hole Device and Lead Design on Deep Brain Stimulation Lead Stability in Benchtop and Ovine Models
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: A market-released deep brain stimulation (DBS) lead and burr hole device (BHD) have been used for more than ten years to provide stable DBS therapy using leads with four equally distributed cylindrical electrodes along the distal lead length. Newer directional leads cluster segmented electrodes at the center of the electrode array. This work tests the hypothesis that improved chronic translational and rotational stability through enhanced BHD design may ensure that these newer directional electrodes remain in a stable orientation near the stimulation target to maintain therapy and maximize opportunities to adjust therapy, if needed. MATERIALS AND METHODS: A new DBS lead system (commercially available in the United States and termed "new" throughout the manuscript) has been developed, and a combination of bench testing (45 product samples tested) and chronic sheep studies (17 animals followed for 13.5 weeks on average) was conducted to test the hypothesis that design changes incorporated into the new DBS system further stabilize the position and orientation of a DBS lead tip compared with a legacy DBS system. RESULTS: The new DBS system demonstrated a 55% relative improvement in chronic lead tip stability compared with the legacy DBS system with over a decade of clinical use. In a bench test, the new system required 79% more applied torque and 203% more lead body revolutions to rotate the lead in the BHD than the legacy system that was not designed to offer rotational stability. CONCLUSIONS: These measurements quantitatively demonstrate that DBS system design can positively improve lead translational and rotational stability and show that system design is an important consideration for future product development.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".