Lay knowledge and epistemic justice in mental health: a critical analysis of the peer support worker in the Spanish State.

Parkinson's disease (PD) is the second most common neurodegenerative disorder globally. The non-motor symptoms of PD can seriously affect the prognosis and quality of life of patients; Parkinson's disease related cognitive impairment (PD-CI) is the most common and disabling non motor symptom in PD patients. Parkinson's disease with mild cognitive impairment (PD-MCI) is a cognitive syndrome in PD patients that lies between normal cognitive function (Parkinson's Disease with Normal Cognitive Function, PD-NC) and Parkinson's disease with dementia (PDD). Clinically, PD-MCI is primarily characterized by impairments in one or more cognitive domains, including memory, attention, working memory, executive function, language abilities, and visuospatial abilities. PDD refers to PD patients with severe cognitive impairment or dementia, primarily characterized by severe deficits in executive function, visuospatial abilities, and attention. With PD progression, is a resulting progression in PD-CI. Prevalence of PD-MCI is approximately 18.9%-38.2%, while its prevalence among non-dementia patients exceeds 50%. Within one year of diagnosis, 24.2%-27.8% of PD-MCI patients may revert back to PD-NC; however, after 20 years, 83% of PD-MCI patients will inevitably progress to PDD. PDD is associated with higher hospitalization rates and costs. As of 2022, nearly $1 trillion in annual global expenses can be attributed to dementia, affecting an estimated 57.4 million people worldwide. It has been reported that the mortality and hospitalization rates of PDD patients are significantly higher than those of PD-NC patients. Currently, there are no disease-modifying treatments or preventive measures available to improve outcomes for PDD patients. As a result, once PDD patients begin to exhibit clinical symptoms, they not only endure higher morbidity and severe decline in quality of life, but their primary caregivers also face significant caregiving burdens and financial strain. PD-MCI represents the earliest detectable stage of cognitive impairment in PD patients that may progress to PDD. Therefore, early identification of the risk of PD-MCI in PD patients is beneficial for early cognitive intervention and slowing the progression to PDD. Although numerous studies have reported risk factors for PD-MCI, including gender, age, education level, disease duration, history of hypertension, diabetes, and hyperlipidemia. Due to inconsistencies among reported factors in the literature, a systematic study of PD-MCI risk factors is lacking. Machine learning (ML), which involves algorithms that iteratively learn from data to identify complex patterns and improve predictive performance, has been increasingly applied to the early prediction of PD-MCI. Therefore, in this study, we used clinical assessment scales from the PD-MDCNC database of the Hubei Parkinson's Disease Research Center, together with blood test data from an affiliated hospital, to develop and compare ten ML-based models for predicting the risk of PD-MCI. The data used in this study were obtained from the Hubei Parkinson's Disease Clinical Research Center database, which is part of the Parkinson's Disease & Movement Disorders Multicenter Database and Collaborative Network in China (PD-MDCNC). This database contains standardized clinical assessment data from Parkinson's disease patients collected across multiple tertiary hospitals in China. It includes more than 50 assessment scales covering motor and non-motor symptoms, quality of life, and other clinical characteristics of PD patients. The overall data completeness exceeds 90%, and all assessments were conducted by trained clinicians, ensuring high data quality and reliability. Website of the database is . Since this database is not open to the public, authorization for data use was obtained from the database administrator prior to data extraction. The study protocol was reviewed and approved by the Institutional Review Board of Xiangyang No.1 People's Hospital and conducted in accordance with the Helsinki Declaration. The patient data extracted from the database includes basic information, medical history, lifestyle history, current medical history, and multiple PD-related assessment scales, as detailed in Appendix Figure 1.