Programming Directional Deep Brain Stimulation in Parkinson’s Disease: A Randomized Prospective Trial Comparing Early versus Delayed Stimulation Steering
Bibliographic record
Abstract
INTRODUCTION: Programming directional leads poses new challenges as the optimal strategy is yet to be established. We designed a randomized control study to establish an evidence-based programming algorithm for patients with Parkinson's disease undergoing subthalamic nucleus deep brain stimulation with directional leads. METHODS: Fourteen consecutive patients were randomized to programming with either early or delayed (i.e., starting with a "ring mode") steered stimulation. Motor scores, number of programming visits, calls to the clinic, battery consumption, and stimulation adjustments required were recorded and compared between groups, using the Wilcoxon signed-ranks test, after 3 months of open-label programming. RESULTS: Thirteen patients (25 electrodes) were included, of which 23 were steerable. Nine out of 14 electrodes allocated to delayed steered stimulation were changed to steered mode due to side effects during the course of the study. No patients (11 electrodes) initially allocated to early steered stimulation were converted to ring mode. The 2 study arms did not differ in any of the considered measures at 3 months. CONCLUSION: Programming with early or delayed steered stimulation is equally effective in the short term. However, delayed steering is less time consuming and is not always needed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".