Is There a Relation Between Caregiver Burden and Cognitive Dysfunction in Huntington’s Disease?
Bibliographic record
Abstract
Abstract Caring for a family member with Huntington’s disease (HD) can be seriously burdensome. Cognitive and neuropsychiatric symptoms that are part of HD can impact the quality of life of caregivers. Therefore, we investigated the relationship between caregiver burden, cognitive impairment and patient characteristics. A retrospective cross-sectional study was performed on 33 adult HD-outpatient-caregiver dyads. We assessed caregiver burden and cognitive functioning of the included patient on the same day with the MCSI and MoCA respectively. For statistical analysis, we performed a network analysis and used descriptive statistics to describe our study sample. Caregivers scored on average 13.5 out of 26 points on the MCSI. The scores on the MoCA of the HD patients varied from 9 to 30 and was on average 22. Our network analysis demonstrated an indirect relationship between cognitive functioning and caregiver burden, in which CAG repeat length and the time since HD has been diagnosed are the primary mediators. We found a negative association between CAG repeat length and cognitive functioning. Furthermore, a relationship was found between higher caregiver burden and psychotropic drug use. We observed an indirect relationship between cognitive functioning and caregiver burden using network analysis. This analysis produces comprehensible results with the variables of interest. This study sheds new light on the components that make up caregiver burden in HD.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".