Stimulating Frontostriatal Circuitry to Treat Motor and Non-Motor Symptoms of Parkinson’s Disease (P6.073)
Bibliographic record
Abstract
OBJECTIVE: We sought to assess patients with a diagnosis of PD along with TRD/psychosis in the motoric (UPDRS-Unified Parkinson’s Disease Rating Scale), cognitive (MOCA-Montreal Cognitive Assessment), and neuropsychiatric (AS-Apathy Scale, GDS-Geriatric Depression Scale, HAMD-17-Hamilton Depression Scale, SAPS-Scale for the Assessment of Positive Symptoms) domains prior to and after an acute course of right unilateral ultrabrief pulse (RULUBP) electroconvulsive therapy (ECT). BACKGROUND: Parkinson’s disease (PD) is a movement disorder frequently associated with neuropsychiatric dysfunction. Despite ECT's efficacy in treating PD, clinicians have been reluctant to use traditional ECT due to its cognitive side-effects. Recent studies in treatment-resistant depression (TRD) have demonstrated that RULUBPECT has a favorable cognitive side-effect profile. DESIGN/METHODS: We assessed change from baseline (BL) to immediate post-treatment (IPT) and one month post-treatment. Due to sample size (n=5), we chose a nominal value of p=0.10 to denote a statistical significance, and used the Wilcoxon signed-rank test (WSRT) to compare matched values at different time-points for each participant. RESULTS: All measures (except MOCA) dropped dramatically acutely and long-term. Mean values from baseline to post-treatment decreased from 33.8 to 8.6(HAMD-17), 60.2 to 23.6(AS), 23.2 to 9.0(GDS), 4.2 to 0.2(SSI), 18.6 to 1.6(SAPS), and 31.4 to 11.75(UPDRS) while MOCA increased from 26.2 to 27.6. Based on WSRT, improvements were statistically significant for HAMD-17, AS, and GDS scales at IPT (p=0.06). UPDRS, SSI, and SAPS trended toward statistical significance. CONCLUSIONS: This open label study suggests that RULUBPECT is safe, and is likely efficacious in treating multiple domains of PD. Study Supported by: R25 DA020537-06 Back, SE & Brady, KT(PI)
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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 | 0.000 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".