Non-motor symptoms in Parkinson’s disease: a practical approach
Bibliographic record
Abstract
Non-motor symptoms of Parkinson's disease (PD) drive disability, reduce quality of life and increase healthcare resource use. Yet, they are often under-recognised in time-limited clinics. We outline an evidence-informed approach for everyday neurology. We propose brief previsit process followed by in-visit prioritisation of one or two high-impact symptoms and simple education/resources for the rest. Psychosis requires clinicians to address triggers, simplify dopaminergic therapy and use PD-safe antipsychotics when required. Mood and anxiety benefit from optimised dopaminergic regimens, selective serotonin reuptake inhibitors/serotonin-norepinephrine reuptake inhibitors, counselling, exercise and online cognitive-behavioural programmes. Cognitive impairment warrants regular screening and medication review; cholinesterase inhibitors are indicated for dementia. Sleep management includes treating contributing factors, hygiene measures and cautious hypnotic use. Pain and autonomic dysfunction require pragmatic, stepwise strategies tailored to real-world practice. We include special considerations for atypical Parkinsonism and caregiver needs. Integrating these steps into routine visits improves safety, function and patient-carer well-being.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".