Sexual dimorphism in Parkinson’s disease: differences in clinical manifestations, quality of life and psychosocial functioning between males and females
Bibliographic record
Abstract
Introduction: Sex-related differences in clinical manifestations and consequences of Parkinson’s disease (PD) have been poorly explored. Better understanding of sexual dimorphism in neurologic diseases such as PD has been announced as a research priority. The aim of our study was to determine independent sex differences in clinical manifestations and subtypes, psychosocial functioning, quality of life (QoL) and its domains between male and female individuals with PD. Patients and methods: A comprehensive list of demographics, motor symptoms and subtypes, nonmotor features, health-related quality of life (HRQoL), psychosocial functioning and general aspects of daily life was assessed in 157 individuals (108 males and 49 females) with idiopathic PD. In order to control for potential confounding variables, we applied Orthogonal Partial Least Squares – Discriminant Analysis (OPLS-DA) to explore the strength of each feature to discriminate male and female patients with PD. Results: While no sex difference was found in the total Unified Parkinson’s Disease Rating Scale (UPDRS) score and cumulative daily dose of levodopa, females had significantly more severe anxiety (mean difference =2.2 [95% confidence interval, CI: 0.5–4.0], P =0.011), worse nutritional status (23.8 [standard deviation, SD =4.2] vs 25.8 [SD =2.6], P =0.003) and poorer QoL (28.3 [SD =15.7] vs 17.9 [SD =14.2], P <0.001). Based on multivariate discriminant analysis, emotional well-being, bodily discomfort, social support, mobility and communication domains of HRQoL, together with anxiety, depression and psychosocial functioning, were the strongest features with more severe/worse status in females after adjustment for potential statistical confounders. Conclusion: Our study provides a comprehensive understanding of sexual dimorphism in PD. Anxiety, depression, specific domains of HRQoL (mobility, emotional well-being, social support and bodily discomfort) and psychosocial functioning were significantly worse in female individuals with PD. Sexual dimorphism in PD highlights the features that are more likely to be affected in each sex and should be specifically targeted when managing male and female individuals with PD. Keywords: Parkinson’s disease, sexual dimorphism, male, female, quality of life, psychosocial functioning
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".