Relative Efficiency of the EQ-5D, HUI2, and HUI3 Index Scores in Measuring Health Burden of Chronic Medical Conditions in a Population Health Survey in the United States
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVE: We sought to compare the ability of the EQ-5D, Health Utilities Index Mark 2 (HUI2), and HUI Mark 3 (HUI3) index scores to discriminate between respondents based on the presence or absence of chronic medical conditions in a population health survey. METHODS: Secondary analyses were conducted with data from a probability sample (n = 3480, mean age: 42.5 years, male: 42.4%, Hispanic: 28.6%) of the 2001 noninstitutionalized US general adult population. F-statistic ratios were used to evaluate the relative efficiency of the EQ-5D, HUI2, and HUI3 in differentiating respondents with or without each of 18 chronic medical conditions, and differentiating respondents with low- or high-burden conditions. RESULTS: In comparing respondents with and without chronic medical conditions, the F-statistic values of these 3 indices were not significantly different, except for EQ-5D versus HUI2 [mean F-statistic ratio: 0.79, 95% confidence interval (CI): 0.59-0.98]. In comparing respondents with a low-burden condition with those with a high-burden condition, the F-statistic values of EQ-5D and HUI2 index scores were similar, while those for EQ-5D versus HUI3 (mean: 0.79; 95% CI: 0.66-0.92) and for HUI2 versus HUI3 (mean: 0.83; 95% CI: 0.71-0.95) were significantly less than 1.0. The overall ceiling effects of the EQ-5D, HUI2, and HUI3 index scores were 48.9%, 15.4%, and 15.3%, respectively. CONCLUSIONS: Although the EQ-5D seems to be marginally less informative, the EQ-5D, HUI2, and HUI3 index scores were generally comparable in determining health burden of chronic medical conditions in this population health survey data.
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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 | 0.023 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it