Non-motor complications in late stage Parkinson’s disease: recognition, management and unmet needs
Bibliographic record
Abstract
Introduction: The burden of non-motor symptoms (NMS) is a major determinant of health-related quality of life in Parkinson’s disease (PD), particularly at its late stage.Areas covered: The late stage is usually defined as the period from unstable advanced to the palliative stage, characterized by a combination of emerging treatment-resistant axial motor symptoms (freezing of gait, postural instability, falls and dysphagia), as well as both non-dopaminergic and dopaminergic NMS: cognitive decline, neuropsychiatric symptoms, aspects of dysautonomia, pain and sleep disturbances (insomnia and excessive day-time sleepiness). Here, the authors summarize the current knowledge on NMS dominating the late stage of PD and propose a pragmatic and clinically focused approach for their recognition and treatment.Expert opinion: The NMS progression pattern is complex and remains under-researched. While dopamine-dependent NMS may improve with dopamine replacement therapy, non-dopamine dependent NMS worsen progressively and culminate at the late stages of PD. Furthermore, some PD specific features could interact negatively with other comorbidities, multiple medication use and frailty – the evaluation of these aspects is important in the creation of personalized management plans in the late stage of PD.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".