Non-motor symptoms in Parkinson's disease: Recognition, diagnosis, and implications for comprehensive management
Bibliographic record
Abstract
Introduction: Parkinson’s disease (PD) is traditionally defined by motor symptoms such as bradykinesia, rigidity, and tremors. However, non-motor symptoms (NMS) are now recognized as central contributors to disability, often preceding motor onset and remaining underdiagnosed. Materials and methods: This narrative review is based on a focused literature analysis of non-motor symptoms in Parkinson’s disease, including neuropsychiatric, cognitive, sensory, sleep, and autonomic domains. Articles were selected for clinical relevance and pathophysiological insights. Results: The reviewed literature indicates that non-motor symptoms are highly prevalent and significantly impact quality of life in Parkinson’s disease. These symptoms often correlate with disease progression and motor fluctuations. Various management strategies have been described, although underdiagnosis remains common due to limited screening in routine clinical practice. Conclusions: Early identification and targeted treatment of non-motor symptoms are critical for optimizing clinical outcomes, improving patient quality of life, and reducing healthcare burdens. Comprehensive clinical assessments and the integration of multidisciplinary care models are essential to meet the complex needs of patients with Parkinson’s disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".