A comparison of health utility measures for the evaluation of multiple sclerosis treatments
Bibliographic record
Abstract
OBJECTIVES: To evaluate the practical application and psychometric properties of three health utility measures in a sample of MS patients with a broad range of neurological disability as measured by the Extended Disability Status Scale (EDSS). METHODS: Patients randomly selected from two MS clinic registries were assessed using standard clinical methods and completed three generic measures of health utility (EQ-5D, HUI Mark III, SF-6D). The proportion of missing data, test/retest reliability, and construct validity of each health utility measure were examined. RESULTS: The assessments were completed by 187 patients. Less than 10% of data were missing for the subscales of the SF-6D (< 3.2%), HUI Mark III (<1.6%), and EQ-5D (< or =7.5%). Severely disabled patients were more likely to omit physical function questions for the SF-6D (20%), and EQ-5D (43%). Retest reliability for the SF-6D (ICC = 0.83), EQ-5D (ICC = 0.81), and HUI Mark III (ICC = 0.87) were adequate for population surveys. Correlations between assessment of clinical function and each health utility measure were strongest for the HUI Mark III (HUI Mark III EDSS rho = -0.77, HUI Mark III ambulation index rho = -0.76, HUI Mark III timed 25 foot walk rho = -0.73, HUI Mark III nine hole peg test rho = -0.65). CONCLUSIONS: The health utility measures were generally feasible and reliable but the HUI Mark III demonstrated highest concordance with the EDSS across the full range of neurological disability. Of the three measures studied, the HUI Mark III may be the most appropriate for cost effectiveness evaluations of MS therapies.
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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 | 0.049 | 0.178 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".