Nonmotor Symptoms in Drug-Induced Parkinsonism and Drug-Naïve Parkinson Disease
Bibliographic record
Abstract
BACKGROUND: The clinical manifestations of drug-induced parkinsonism (DIP) and Parkinson disease (PD) are nearly indistinguishable, making it difficult to differentiate DIP from PD, especially in the early stages. We compared non-motor symptoms between patients with DIP and those with drug-naïve PD in the early stages using the Non Motor Symptoms Scale (NMSS). METHODS: We prospectively enrolled 28 patients with DIP, 35 patients with drug-naïve PD, and 32 controls with no history of neurological diseases or related medical problems. We investigated demographic characteristics, medical and drug history, parkinsonian motor symptoms, and non-motor symptoms. We used the NMSS to evaluate non-motor symptoms in all patients. RESULTS: The total NMSS scores were higher in patients with PD than those with DIP, as were the scores for certain domains, including the cardiovascular, sleep/fatigue, urinary, sexual, and miscellaneous domains. When controlling for age and gender, the correlation analysis revealed that scores for urinary symptoms (urgency, frequency and nocturia), sleep disturbances (daytime sleep, restless legs), concentration, taste or smell were significantly associated with PD. CONCLUSIONS: Our data suggest that non-motor symptoms, particularly urinary symptoms, excessive daytime sleepiness, restless leg syndrome, attention deficit and hyposmia may be helpful to differentiate between DIP and PD in the early stages.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".