Advanced Therapies for the Management of Dopamine Dysregulation Syndrome in Parkinson's Disease
Bibliographic record
Abstract
ABSTRACT Background Dopamine Dysregulation Syndrome (DDS) is an adverse non‐motor complication of dopamine replacement therapy in Parkinson's disease. The current literature on this syndrome is limited, and it remains underdiagnosed and challenging to manage. Objective To assess the role of advanced therapies in the management of DDS. Methods We performed a retrospective chart review and identified patients who fit the inclusion criteria for DDS. They were classified according to risk factors that have been identified in the literature, motor and complication scores, intervention (medical or surgical) and outcome. Multivariate analyses were performed to analyze these characteristics. Results Twenty‐seven patients were identified (23 males, mean age of onset: 49 ± 8.8 years). Average levodopa equivalent daily dose was 1916.7 ± 804 mg and a history of impulse control disorders, psychiatric illness, and substance abuse was present in 89%, 70% and 3.7% of the patients, respectively. Overall 81.5% of patients had symptom resolution at follow up, on average 4.8 ± 3.5 years after management, with medication only (7/9), levodopa‐carbidopa intestinal gel (1/3), deep brain stimulation of subthalamic nucleus (10/13), or globus pallidus pars interna (2/2). Reduction of medications occurred with deep brain stimulation of subthalamic nucleus (P = 0.01) but was associated with a relapse in two patients. Conclusion Although the small sample size of some subgroups limits our ability to draw meaningful conclusions, our results did not suggest superiority of a single treatment option. Advanced therapies including deep brain stimulation can be considered in patients with DDS refractory to conservative measures, but outcome is variable and relapse is possible.
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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 | 0.001 |
| 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".