Addiction-Like Manifestations and Parkinson's Disease: A Large Single Center 9-Year Experience
Bibliographic record
Abstract
OBJECTIVE: Characterize potential risk factors and the relationship of dopamine agonist (DA) withdrawal syndrome (DAWS), dopamine dysregulation syndrome (DDS), and impulse control disorders (ICDs) in Parkinson's disease (PD). METHODS: A retrospective chart review categorized cases into three groups: DAWS, DDS, and ICDs. RESULTS: A total of 1,040 subjects met inclusion criteria. There were 332 subjects with a history of tapering DAs and 26 (7.8%) developed DAWS. Fourteen (1.3%) and 89 (8.6%) met the criteria for both DDS and ICD. Subjects with DAWS, DDS, and ICDs had a higher baseline dose of DA, levodopa, and total dopaminergic medication (p < .05), compared to those without the three conditions. DDS was found to be related to the DAWS group (p < .001). When comparing to the PD population without DDS, younger age at onset of PD (p = .027), presence of DAWS (p < .001), ICDs (p = .003), and punding (p = .042) were all correlated with the DDS group, while male sex (p = .045), younger age at onset of PD (p < .001), presence of DAWS (p < .001), and presence of DDS (p = .001) and punding (p < .001) were related to the ICD group. CONCLUSIONS: There was a strong relationship between DAWS, DDS, and ICD in this large PD cohort. Dopaminergic therapy in a subset of PD patients was strongly associated with addiction-like behavioral issues.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".