P.042 Dopamine Dysregulation Syndrome in Parkinson’s Disease and its Management with Advanced Therapies
Bibliographic record
Abstract
Background: Dopamine Dysregulation Syndrome (DDS) is an adverse non-motor complication of dopamine replacement therapy in Parkinson’s Disease. The current literature on DDS is limited, and it remains underdiagnosed and challenging to manage. Methods: We performed a retrospective chart review and classified patients according to risk factors that have been identified in the literature, UPDRS scores, intervention and outcome. Univariate analyses were performed to quantify these characteristics. Results: Prior psychiatric illness was identified in 70% of patients, impulse control disorder in 89% and substance abuse in 3.7%. Interventions included reduction of dopamine therapy (88.9%), deep brain stimulation (DBS) of the subthalamic nucleus (STN, 48.1%) or globus pallidus interna (GPi, 7.4%), and levodopa-carbidopa intestinal gel (LCIG) infusion (11.1%). Baseline UPDRS IV before treatment and MDS III after treatment were not significant between intervention groups (p=0.09 and p=0.13 respectively). Overall 88.9% patients improved at follow up, with medication only (75%), STN DBS (100%), GPi DBS (100%) and LCIG (33%). Relapse rate was 18.2%, in the STN group only. Conclusions: Our results suggest that GPi DBS, in concurrence with dopaminergic medication reduction, is the most effective intervention. STN DBS might be also beneficial although the associated medications reduction causes DDS relapse in a subgroup of patients.
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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) | 0.003 | 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".