Impact of Non-motor Symptoms on the Quality of Life in Parkinson's Disease Patients
Bibliographic record
Abstract
Parkinson’s disease (PD) affects about 1–2% of the population over 65 years of age and up to 3–5% of people 85 years of age and older. PD is a neurodegenerative disease characterized by a combination of motor and non-motor symptoms. NMS contribute to overall healthcare costs and have a profound impact on the quality of life of both patients and caregivers. Their improved management has been identified as a major unmet medical need. The main clinical symptoms of PD are well understood, but it is necessary to continue studying the changes of symptoms as it progresses. The nonmotor symptoms (NMS) of Parkinson’s disease (PD) are important factors for quality of life (QOL). Few studies on NMS have been conducted in Asian PD patients. The aims of the research. To study the structure of non-motor symptoms (NMS) at the early and last stages of PD, to determine the frequency and clinical significance of the NMS at different stages of PD, comparing the severity of NMS in PD with the severity of the same symptoms in the natural aging, to study the quality of life (QOL) of patients with the impact assessment of the NMS and the assessment of motor symptoms on this features. The material and methods. The study included 40 patients with PD and 15 patients without neurodegenerative disorders (control group). The degree of movement disorders severity was assessed using UPDRS scale. Cognitive function was assessed using the Montreal Cognitive Rating Scale (MoCA). To identify and assess the severity of NMS questionnaire used NMSS. Assessment of quality of life of patients was carried out by PDQ-26 questionnaire. Results. There was a significant difference between the intensity of the NMS in PD patients and the control group. Intensity of NMS significantly correlated with disease stage, disease duration, with the points on the UPDRS scale. NMS had at least some impact on quality of life for 84% of the respondents; 48% indicated that NMS represented a greater challenge than motor symptoms. Conclusion. NMS significantly affect the quality of life, their severity and structure varies considerably from early to late stage PD, and they are the result of a neurodegenerative process, and not the natural aging process.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".