Factors associated with the development of impulse compulsive disorders in Parkinson patients
Bibliographic record
Abstract
OBJECTIVE: The aim of this study is to identify the prevalence of impulse compulsive disorder (ICD) in Parkinson's disease (PD) patients and to study potential associative factors that may be related to the onset of ICD while on parkinsonian medications. METHOD: The study was conducted in two parts. In Part A, the prevalence of ICD in 140 PD patients was first assessed, followed by identifying any common variables that may be associated with the development of ICD. Finally, using a detailed chart review, Part B of the study examined the prevalence of ICD in all patients who presented with identical risk factors gathered in Part A. RESULTS: Of the 140 patients, 8 patients developed symptoms of ICD. Seven of these patients were found to have five common variables that included gender (males), stages 1-2 of PD, young onset of PD, maximum dosage of the drug and the use of dopamine agonists (DAs). Of the 140 patients, 22 patients fit the above-mentioned five criteria and of those 22 patients, 33% developed symptoms of ICD. CONCLUSION: The use of DA therapy in the treatment of PD patients should be carefully monitored, especially in younger male patients who exhibit early signs of parkinsonian symptoms. As such, it is crucial for physicians to adjust DA dosages while also seeking alternative treatments to avoid the risk of ICD while on DAs.
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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 | 0.003 |
| 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 | 0.000 |
| Research integrity | 0.000 | 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".