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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".