Considerations for Patient and Target Selection in Deep Brain Stimulation Surgery for Parkinson’s Disease
Bibliographic record
Abstract
Deep brain stimulation (DBS) is an effective treatment for improving motor symptoms of Parkinson’s disease among well-selected patients. Over the past 30 years, several anatomical regions have been targeted with DBS based on prior experience in lesional neurosurgery and characteristic changes in subcortical motor regulation in Parkinson’s disease. In this chapter, we provide an overview of the patient selection process and surgical procedure for DBS. We also discuss the various surgical targets for DBS in patients with Parkinson’s disease. The subthalamic nucleus and the globus pallidus interna are the most common surgical targets among patients with Parkinson’s disease and have equivalent beneficial effects on motor symptoms. Most studies report 30–60% improvement in motor score evaluations after DBS among well-selected patients. After subthalamic nucleus DBS, patients are able to reduce medications by 50% on average. In patients with globus pallidus interna DBS, stimulation has an anti-dyskinetic effect, although medication doses remain similar. DBS of the subthalamic nucleus is generally avoided in patients with a history of depression or neurocognitive impairment. Thalamic DBS ameliorates tremor, but has little effect on bradykinesia or rigidity. Finally, the pedunculopontine nucleus DBS is an emerging experimental treatment for postural and gait instability in Parkinson’s disease.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.009 |
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".