Premotor and nonmotor features of Parkinson's disease
Bibliographic record
Abstract
PURPOSE OF REVIEW: This review highlights recent advances in premotor and nonmotor features in Parkinson's disease, focusing on these issues in the context of prodromal and early-stage Parkinson's disease. RECENT FINDINGS: Although Parkinson's disease patients experience a wide range of nonmotor symptoms throughout the disease course, studies demonstrate that nonmotor features are not solely a late manifestation. Indeed, disturbances of smell, sleep, mood, and gastrointestinal function may herald Parkinson's disease or related synucleinopathies and precede these neurodegenerative conditions by 5 or more years. In addition, other nonmotor symptoms such as cognitive impairment are now recognized in incident or de-novo Parkinson's disease cohorts. Many of these nonmotor features reflect disturbances in nondopaminergic systems and early involvement of peripheral and central nervous systems, including olfactory, enteric, and brainstem neurons as in Braak's proposed pathological staging of Parkinson's disease. Current research focuses on identifying potential biomarkers that may detect persons at risk for Parkinson's disease and permit early intervention with neuroprotective or disease-modifying therapeutics. SUMMARY: Recent studies provide new insights into the frequency, pathophysiology, and importance of nonmotor features in Parkinson's disease as well as the recognition that these nonmotor symptoms occur in premotor, early, and later phases of Parkinson's disease.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".