Genetic and Clinical Predictors of Deep Brain Stimulation in Young‐Onset Parkinson's Disease
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Bibliographic record
Abstract
Abstract Objective In a cohort of patients with young‐onset Parkinson's disease ( PD ), the authors assessed (1) the prevalence of genetic mutations in those who enrolled in deep brain stimulation ( DBS ) programs compared with those who did not enroll DBS programs and (2) specific genetic and clinical predictors of DBS enrollment. Methods Subjects were participants from 3 sites (Columbia University, Rush University, and the University of Pennsylvania) in the Consortium on Risk for Early Onset Parkinson's Disease ( CORE ‐ PD ) who had an age at onset < 51 years. The analyses presented here focus on glucocerebrosidase ( GBA ), leucine‐rich repeat kinase 2 ( LRRK 2 ), and parkin ( PRKN ) mutation carriers. Mutation carrier status, demographic data, and disease characteristics in individuals who did and did not enroll in DBS were analyzed. The association between mutation status and DBS placement was assessed in logistic regression models. Results Patients who had PD with either GBA , LRRK 2 , or PRKN mutations were more common in the DBS group (n = 99) compared with the non‐ DBS group (n = 684; 26.5% vs. 16.8%, respectively; P = 0.02). In a multivariate logistic regression model, GBA mutation status (odds ratio, 2.1; 95% confidence interval, 1.0–4.3; P = 0.05) was associated with DBS surgery enrollment. However, when dyskinesia was included in the multivariate logistic regression model, dyskinesia had a strong association with DBS placement (odds ratio, 3.8; 95% confidence interval, 1.9–7.3; P < 0.0001), whereas the association between GBA mutation status and DBS placement did not persist ( P = 0.25). Conclusions DBS populations are enriched with genetic mutation carriers. The effect of genetic mutation carriers on DBS outcomes warrants further exploration.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it