Mapping the landscape of caregiver burden in Huntington's Disease: Current evidence and future directions
Bibliographic record
Abstract
IntroductionHuntington's Disease (HD) is a rare neurodegenerative disease that profoundly affects both individuals diagnosed with the condition and their caregivers. This review aims to examine the burden experienced by informal caregivers of patients with HD and identify relevant factors that exacerbate or mitigate this burden.MethodsThe PRISMA guidelines were followed, and an extensive search of electronic databases (PubMed, Science Direct, Taylor & Francis) was undertaken to identify original research articles published in English between January 2005 and April 2025. Two reviewers independently screened the studies. The quality of the studies was evaluated using the Critical Appraisal Program (CASP). Data were extracted, and a narrative synthesis was conducted to integrate and summarize the results.ResultsTwelve studies were included in the review involving 569 caregivers of patients with HD. Studies were conducted in Europe, the United States, Canada, and Australia, with one taking place in South Korea. Patient demographics, caregiver characteristics, disease-related factors, disrupted family dynamics, caregivers' compromised mental health, and availability to access networks are related to caregiver burden. Neuropsychiatric symptoms and the hereditary nature of the disease have been identified as important correlates of caregiver strain.ConclusionCaring for individuals with HD involves a distinct and multifaceted burden shaped by both the nature of the illness and inadequate external support. Addressing this requires future research to develop tailored interventions and tools that reflect the unique needs of HD caregivers across varying stages and cultural contexts.
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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 | 0.030 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".