Predictors of Future Deep Brain Stimulation Surgery in de novo Parkinson's Disease
Bibliographic record
Abstract
Abstract Background Deep brain stimulation (DBS) surgery is offered to a subset of Parkinson's disease (PD) patients. It is unclear if there are features at diagnosis that predict future DBS surgery. Objective To assess predictors of eventual DBS surgery in de novo PD patients. Methods Subjects from the Parkinson's Progression Marker Initiative (PPMI) database with newly diagnosed, sporadic PD (n = 416) were identified and stratified by their eventual DBS status (DBS+, n = 43; DBS‐, n = 373). A total of 50 baseline clinical, imaging, and biospecimen features were extracted for each subject and cross‐validated lasso regression was used for feature reduction. Multivariate logistic regression assessed their relationship with DBS status and a receiver operating characteristic curve evaluated model performance. Linear mixed effect models assessed disease progression over 4 years in DBS+ and DBS‐ patients. Results Age at symptom onset, Hoehn and Yahr (H&Y) stage, tremor score, and ratio of CSF Tau to amyloid‐beta 1–42 (Tau: Ab) were identified as important baseline features for predicting DBS surgery. Each independently predicted DBS surgery (area under the curve = 0.83). DBS‐ patients had faster memory decline (P < 0.05), while DBS+ patients had faster decline in H&Y stage (P < 0.001) and motor scores (P < 0.05) prior to surgery. Conclusion The identified features may be used for early identification of patients who may be surgical candidates during the course of their disease. Disease progression in these groups reflects surgical eligibility criteria, with DBS‐ patients having more rapid decline in memory while DBS+ patients experienced a faster decline in motor scores prior to DBS surgery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".