Contribution of neuropsychiatric symptoms in Parkinson’s disease to different domains of caregiver burden
Bibliographic record
Abstract
INTRODUCTION: Caregiver burden is high among caregivers of PD patients (CPD). Neuropsychiatric symptoms are leading contributors to CPD burden, but whether different symptoms differentially impact domains of caregiver burden is not known. Our objective was to examine which neuropsychiatric symptoms and demographic factors contribute to different domains of caregiver burden in PD. METHODS: This was a cross-sectional online survey study. Participants were recruited from the Fox Insight (FI) study and were eligible if they identified themselves as a CPD. The primary outcome was the Caregiver Burden Inventory (CBI) total score and its 5 sub-domain scores. The Neuropsychiatric Inventory Questionnaire (NPI-Q) assessed caregiver-reported neuropsychiatric symptoms in the care recipient. Multivariable linear regression models were used to characterize the associations between NPI-Q symptom severity scores and CBI scores. Covariates were caregiver age, sex, education, and caregiving duration. RESULTS: = 39.2%. Anxiety severity had the largest effect size [standardized β (sβ) = 0.224] on the time-dependency domain, which was also associated with female sex (sβ = - 0.133) and age (sβ = 0.088). Severity of disinhibition (sβ = 0.218), agitation (sβ = 0.199), and female sex (sβ = 0.104) were associated with greater emotional burden. CONCLUSION: Our findings indicate that demographic characteristics and specific neuropsychiatric symptoms contribute differentially to domains of caregiver burden. Tailored interventions to support CPD are needed.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".