Effects of Diabetes Mellitus on Motor and Non-Motor Symptoms in Parkinson’s Disease: A Cross-Sectional Study
Bibliographic record
Abstract
Introduction: While there is a growing body of evidence indicating a potential connection between Parkinson’s disease and diabetes mellitus, there is a lack of focus on investigating how diabetes correlates with the severity of both motor and non-motor symptoms in Parkinson’s disease. Objective: This study examined and contrasted both motor and non-motor symptoms in patients diagnosed with Parkinson’s disease, stratified by the presence or absence of diabetes. Methods: A total of 40 Parkinson’s disease patients, divided into two groups (with and without diabetes), were assessed using various scales, including the Movement Disorders Society – Unified Parkinson’s Disease Rating Scale, Scales for Outcomes in Parkinson’s Disease - Autonomic Dysfunction and Non-Motor Symptoms, Beck Depression Inventory, Montreal Cognitive Assessment, and Parkinson’s Disease Questionnaire-39. Demographic and clinical characteristics were also recorded. Statistical analyses included t-tests, Mann-Whitney U tests, and Fisher’s exact test. Results: Significant differences were observed in the motor sub-score of postural instability and gait disturbance symptoms, autonomic total scores, urinary function domain, depression scores, and quality of life in the mobility and emotional domains between the Parkinson’s disease non-diabetes, and Parkinson’s disease - diabetes groups. Conclusions: Our study unveiled differences in motor and Non-Motor Symptoms among patients with Parkinson’s disease and diabetes, underscoring the influence of diabetes on manifestations 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 | 0.002 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".