Autonomic symptoms in early-stage Parkinson’s patients and their relationship with cognition and disease parameters
Bibliographic record
Abstract
Aims: Autonomic dysfunction is a prevalent feature throughout various stages of the disease and can significantly exacerbate the overall impact of the condition. Moreover, it is linked to accelerated disease advances and diminished vitality rates in individuals with Parkinson’s disease (PD). The main goal of this study is to evaluate the prevalence of autonomic symptoms and cognitive findings and investigate their associations with disease-related factors in early-stage PD patients. Methods: A total of 49 individuals diagnosed with PD were enrolled in this study. Disease severity was assessed using the Unified Parkinson’s Disease Rating Scale (UPDRS), and the disease stage was determined through the modified Hoehn & Yahr Rating Scale (mHYRS). By the mH&Y scale, only individuals in the early stages (≤2.5) of the disease were included in this investigation. The evaluation of autonomic symptoms in PD was conducted using the Scales for Outcomes in Parkinson’s Disease for Autonomic Symptoms (SCOPA-AUT) scale. The cognitive functions of the patients were assessed utilizing the Turkish version of the Montreal Cognitive Assessment Scale (MOCA-TR). Results: The study included 44% (n=22) females and 56% (n=27) males as participants. The average age was 61.5±10.1 years. The mean SCOPA-AUT score was 18.9±9.36, with the most prevalent autonomic symptoms related to the gastrointestinal system. A positive correlation was shown with autonomic symptoms and disease stages (p=0.024, r=0.322). However, no significant relationship was found between autonomic symptoms, other disease parameters, and cognition. We observed a notable inverse correlation between the disease stage and cognitive status (p=0.003, r=-0.417). Conclusion: Our study concluded that autonomic dysfunctions manifest from the early stages of Parkinson’s disease and can intensify as the disease progresses. Identifying and addressing these dysfunctions at an early stage would play a pivotal role in lessening the overall impact of the 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".