Estimating health state utilities in Duchenne muscular dystrophy using the health utilities index and EQ-5D-5L
Bibliographic record
Abstract
Abstract Background The progression of Duchenne muscular dystrophy (DMD) is characterized by loss of ambulation, respiratory insufficiency, cardiomyopathy, and early mortality. DMD profoundly impacts health-related quality-of-life (HRQoL). However, few health state utility data exist; published utilities tend to be derived from small samples for a limited number of health states and are often based on caregiver-reported patient health status. This study estimated utility values for varied clinical and functional health states in DMD, based on patient-reported health status. Methods Individuals with DMD in the US aged 12–40 years completed the EQ-5D (5-level) and Health Utilities Index (HUI) preference-based instruments. Based on responses to a clinical questionnaire, participants self-classified into functional health states according to level of lower and upper limb function, use of respiratory support, and presence of cardiomyopathy. Mean [standard deviation (SD)] utility and EQ-5D visual analogue scale (VAS) scores were estimated according to health state; and median (interquartile range) attribute levels calculated to understand which domains of health are most severely affected in DMD. Results Of 63 males with DMD, mean (SD) age was 19.8 (6.1) years and 11 (17.5%) were ambulatory. Mean (SD) utility values were 0.92 (0.08; HUI2), 0.84 (0.20; HUI3), and 0.84 (0.13; EQ-5D) for ambulatory patients without cardiomyopathy (n = 10). For non-ambulatory patients with moderately impaired upper limb function, night and daytime ventilation without cardiomyopathy, mean (SD) utilities were 0.49 (0.07) for the HUI2, 0.16 (0.15) for the HUI3 and 025 (0.14) for the EQ-5D. Mean (SD) VAS scores for the same health states were 91 (9) and 83 (21), respectively. In addition to impairments in mobility/ambulation, and self-care, attributes like usual activities and pain also showed notable effects of DMD. Conclusions In DMD, although a relationship between disease progression and HRQoL is observed, there is large variability in utility within functional health states, and across instruments. Utility values for less severe non-ambulatory health states described by level of upper limb function are novel. These utility values, derived based on direct patient feedback rather than from caregiver report, are relevant to individuals of varying functional statuses and augment scarce DMD-specific utility data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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 teacher head, 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".