Deep Brain Stimulation in Patients With Mutations in Parkinson's Disease–Related Genes: A Systematic Review
Bibliographic record
Abstract
ABSTRACT Background Deep brain stimulation (DBS) is an effective treatment for Parkinson's disease (PD), and careful selection of candidates is a key component of successful therapy. Although it is recognized that factors such as age, disease duration, and levodopa responsiveness can influence outcomes, it is unclear whether genetic background should also serve as a parameter. Objectives The aim of this systematic review is to explore studies that have evaluated DBS in patients with mutations in PD‐related genes. Methods We performed a selective literature search for articles regarding the effects of DBS in autosomal dominant or recessive forms of PD or in PD patients with genetic risk factors. Data regarding changes in motor and nonmotor scores and the presence of adverse events after the stimulation were collected. Results A total of 25 studies were included in the systematic review, comprising 135 patients. In the shorter term, most patients showed marked or satisfactory response to subthalamic DBS, although leucine rich repeat kinase 2 carriers of R114G mutations had higher rates of unsatisfactory outcome. Longer term follow‐up data were scarce but suggested that motor benefit is sustained. Patients with the glucosidase beta acid (GBA) mutation showed higher rates of cognitive decline after surgery. Motor outcome was scarce for pallidal DBS. Few adverse events were reported. Conclusions Subthalamic DBS results in positive outcomes in the short term in patients with Parkin, GBA, and leucine‐rich repeat kinase 2 (non‐R144G) mutations, although the small sample size limits the interpretation of our findings. Longer and larger cohorts of follow‐up, with broader nonmotor symptom evaluations will be necessary to better customize DBS therapy in this population.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".