Interleaving Stimulation in Parkinson’s Disease, Tremor, and Dystonia
Bibliographic record
Abstract
BACKGROUND/AIMS: Interleaving stimulation (ILS) in deep brain stimulation (DBS) provides individualized stimulation of 2 contacts delivered in alternating order. Currently, limited information on the utility of ILS exists. The aims of this study were to determine the practical applications and outcomes of ILS DBS in Parkinson's disease (PD), tremor, and dystonia. METHODS: We performed a single-center, unblinded, retrospective chart review of all patients with DBS attempted on ILS at our referral center assessing for rationale and outcomes. RESULTS: Fifty patients (PD, n = 27; tremor, n = 7; dystonia, n = 16 patients) tried ILS for 2 rationales: management of adverse effects (n = 29) and to improve clinical efficacy (n = 21). A total of 19 patients demonstrated improvement with ILS for adverse effect management predominately for the treatment of dyskinesias (n = 12). In the vast majority of dyskinetic patients, a contact added into the rostral zona incerta with ILS was performed. Nine out of 21 patients demonstrated improved clinical efficacy with ILS with all 6 PD patients who tried ILS for this rationale demonstrating benefit. CONCLUSIONS: In PD, ILS provided benefits for dyskinesias and parkinsonism, with minimal improvement of other adverse effects. In tremor and dystonia, marginal effects in terms of mitigation of adverse effects and improvement of clinical outcomes were evident.
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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 | 0.000 | 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) | 0.001 | 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".