Neuropsychiatric Symptoms and Caregiver's Burden in Parkinson's Disease Patients in a Tertiary Care Teaching Hospital in South India
Bibliographic record
Abstract
BACKGROUND: In patients with Parkinson's disease (PD), the occurrence of motor and non-motor symptoms increases with disease progression. The range of neuropsychiatric symptoms (NPS) vary among individuals and can be burdensome for caregivers. Only a few studies have identified the contributing factors of NPS and caregiver burden in India. OBJECTIVES: We aimed to study the clinical profile, disability, and predictive factors of NPS in PD patients and associated caregiver's burden. METHODS AND MATERIAL: This was a cross-sectional observational study carried out in PD patients and their respective caregivers attending a movement disorder clinic in a tertiary care teaching hospital in Kerala. A total of 104 patients diagnosed with idiopathic PD receiving levodopa therapy and who had a primary caregiver were enrolled in the study. Structured questionnaires were administered to both patients and caregivers to collect data. Data analysis was done using an independent t-test, linear, and multiple regression analysis. RESULTS: Among 104 patients recruited for the study, 61.5% of patients had shown at least one NPS and 40.44% showed multiple NPS. Results from the study showed that depression is the primary NPS occurring in IPD patients (55.8%) followed by irritability, anxiety, and apathy. On linear regression models, the prime determinant of NPS was the Everyday Abilities Scale for India (EASI). For caregiver burden, the main determinants were the presence of NPS, duration of caregiving, EASI, and RBDSQ score. CONCLUSIONS: NPS in PD are highly associated with and are determinants of caregiver burden. Detailed assessment and specific interventions aimed at NPS could alleviate caregiver burden.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".