Estimating health state utilities in Duchenne muscular dystrophy using the health utilities index and EQ-5D-5L
Bibliographic record
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.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".