H49 The Huntington’s disease quality of life battery for carers (HDQOL-CS): evidence from the Huntington’s disease burden of illness (HDBOI) study for Europe
Bibliographic record
Abstract
Background Huntington’s Disease (HD) progresses over time, impacting mental health, work productivity and inter-personal relationships of the person with HD (PwHD) and their caregivers. We explore the impact of HD on caregiver’s quality of life (QoL) using the Huntington’s Disease Quality of Life Battery for Carers (HDQoL-Cs) tool. Methods The short version of the HDQoL-Cs tool was part of the caregiver questionnaire of the HDBOI study. It has 23 items divided into two components: ‘satisfaction with life’ and ‘feelings about living with HD’. Each item ranges from 0-10 (higher scores reflecting better QoL), and component scores result from the mean score of corresponding items. Caregivers were categorized into three groups according to the disease stage of the PwHD, categorization was based on the opinion of the treating physician. Differences in QoL were explored descriptively using ANOVA tests. Results The sample has 434 caregivers (Table 1), of which 36% were caregivers of early (ES), 36% of mid (MS) and 27% of advance (AS) stage. Mean score of ‘satisfaction with life’ decreased for more advanced stages: 6.00, 5.74 and 5.38 for ES, MS and AS respectively [p< 0.05]. Satisfaction with treatment and social environment were the key items driving the satisfaction score. Similar results observed for ‘feelings about living with HD’ domain: 5.94, 5.31, and 5.12 [p< 0.05]. Stress and exhaustion were the most reported feelings experienced by caregivers. Conclusion Our results quantify the substantial humanistic burden associated with caregiving duties and highlight that the healthcare and psychosocial support needs of PwHD and their families remain largely unmet.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".