Do generic utility measures capture what is important to the quality of life of people with multiple sclerosis?
Bibliographic record
Abstract
PURPOSE: The three most widely used utility measures are the Health Utilities Index Mark 2 and 3 (HUI2 and HUI3), the EuroQol-5D (EQ-5D) and the Short-Form-6D (SF-6D). In line with guidelines for economic evaluation from agencies such as the National Institute for Health and Clinical Excellence (NICE) and the Canadian Agency for Drugs and Technologies in Health (CADTH), these measures are currently being used to evaluate the cost-effectiveness of different interventions in MS. However, the challenge of using such measures in people with a specific health condition, such as MS, is that they may not capture all of the domains that are impacted upon by the condition. If important domains are missing from the generic measures, the value derived will be higher than the real impact creating invalid comparisons across interventions and populations. Therefore, the objective of this study is to estimate the extent to which generic utility measures capture important domains that are affected by MS. METHODS: The available study population consisted of men and women who had been registered after 1994 in three participating MS clinics in Greater Montreal, Quebec, Canada. Subjects were first interviewed on an individualized measure of quality of life (QOL) called the Patient Generated Index (PGI). The domains identified with the PGI were then classified and grouped together using the World Health Organization's International Classification of Functioning, Disability and Health (ICF), and mapped onto the HUI2, HUI3, EQ-5D and SF-6D. RESULTS: A total of 185 persons with MS were interviewed on the PGI. The sample was relatively young (mean age 43) and predominantly female. Both men and women had mild disability with a median Expanded Disability Status Scale (EDSS) score of 2. The top 10 domains that patients identified to be the most affected by their MS were, work (62%), fatigue (48%), sports (39%), social life (28%), relationships (23%), walking/mobility (22%), cognition (21%), balance (14%), housework (12%) and mood (11%). The SF-6D included the most number of domains (6 domains) important to people with MS, followed by the EQ-5D (4 domains) and the HUI2 (4 domains) and then the HUI3 (3 domains). The mean and standard deviation (SD) for the PGI, EQ-5D and the SF-6D were 0.50 (SD 0.25), 0.69 (0.18) and 0.69 (0.13), respectively. The magnitude of difference between the PGI and the generic utility measures was large and statistically significant. CONCLUSION: Although the generic utility measures included certain items that were important to people with MS, there were several that were missing. An important consequence of this mismatch was that values of QOL derived from the PGI were importantly and significantly lower than those estimated using any of the generic utility measures. This could have a substantial impact in evaluating the effect of interventions for people with MS.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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) | 0.000 | 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".