Outcomes of Patients with Parkinson Disease and Pathological Gambling
Bibliographic record
Abstract
OBJECTIVE: To determine the outcomes of patients with Parkinson disease (PD) with pathological gambling (PG) from one Canadian Movement Disorders Clinic. METHODS: Assessments were performed in-person during routine clinic visits of all patients currently followed by one neurologist (OS). Pathological gambling was defined according to DSM-IV-TR criteria. Chart review was performed to obtain details on medication use, dosages, and patient demographics. Follow-up of patients with PG collected information on gambling behavior, PG management interventions, medications, treatment, and psychosocial outcomes. RESULTS: 146 patients were surveyed with an overall prevalence of PG of 4.1% (6/146). The rate of pathological gambling for those patients on dopamine agonist therapy (DA) was 8.1% (6/74). Only patients who were recreational gamblers prior to starting DA developed PG. All PG patients discontinued, decreased, or switched to another DA, and experienced a partial or full remission of PG. 3 (50%) patients described financial losses of $100,000 or more, and 75% (3/4) patients described significant marital stresses. At follow-up (August 2008), 4 of the 6 patients with PG continued to gamble in a controlled fashion despite medication changes. No significant difference in levodopa equivalent daily dose (LEDD) pre- and post-PG were observed; however, the relative amount of DA was decreased (p= 0.0593), while levodopa was relatively increased (p= 0.5277). Despite control of PG, patients still experience financial and marital strains. CONCLUSIONS: DA (in combination with levodopa) was associated with a significantly higher prevalence of PG in PD, particularly in patients who were recreational gamblers previously. Despite control of PG, patients continued to experience significant financial and marital stresses that should be regularly enquired upon in follow-up care and managed appropriately.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".