Axial Impairment Following Deep Brain Stimulation in Parkinson’s Disease: A Surgicogenomic Approach
Bibliographic record
Abstract
BACKGROUND: Postoperative outcome following deep brain stimulation (DBS) of the subthalamic nucleus is variable, particularly with respect to axial motor improvement. We hypothesized a genetic underpinning to the response to surgical intervention, termed "surgicogenomics". OBJECTIVE: We aimed to identify genetic variants associated with clinical heterogeneity in DBS outcome of Parkinson's disease (PD) patients that could then be applied clinically to target selection leading to improved surgical outcome. METHODS: Retrospective clinical data was extracted from 150 patient's charts. Each individual was genotyped using the genome-wide NeuroX array tailored to study neurologic diseases. Genetic data were clustered based on surgical outcome assessed by comparing pre- and post-operative scores of levodopa equivalent daily dose and axial impairment at one and five years post-surgery. Allele frequencies were compared between patients with excellent vs. moderate/poor outcomes grouped using a priori defined cut-offs. We analyzed common variants, burden of rare coding variants, and PD polygenic risk score. RESULTS: NeuroX identified 2,917 polymorphic markers at 113 genes mapped to known PD loci. The gene-burden analyses of 202 rare nonsynonymous variants suggested a nominal association of axial impairment with 14 genes (most consistent with CRHR1, IP6K2, and PRSS3). The strongest association with surgical outcome was detected between a reduction in levodopa equivalent daily dose and common variations tagging two linkage disequilibrium blocks with SH3GL2. CONCLUSION: Once validated in independent populations, our findings may be implemented to improve patient selection for DBS in PD.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".