A Comparison of the Health Utilities Indices Mark 2 and Mark 3 in Type 2 Diabetes
Bibliographic record
Abstract
PURPOSE: Past research into health-related quality of life (HRQL) in diabetes using preference-based measures, such as the Health Utilities Index Mark 2 (HUI2) or the Health Utilities Index Mark 3 (HUI3), is sparse. Important differences between the HUI2 and HUI3 could lead to differences in their abilities to capture HRQL deficits in type 2 diabetes. This analysis compared the extent to which the HUI2 and HUI3 detect differences associated with varying levels of disease severity or advancement in type 2 diabetes. METHODS. This analysis was conducted as part of using baseline, cross-sectional data from a larger, prospective, controlled study of an intervention to improve care for individuals with type 2 diabetes in rural communities in Alberta, Canada. The HU12 and HUI3 were self-administered to 372 community-dwelling individuals with type 2 diabetes. RESULTS: Relative to HUI2 scores, larger differences in overall HUI3 scores were seen for comparisons for individuals presumed to have more advanced disease. The pain attribute of the HUI3 categorized a larger proportion of individuals as moderately to severely impaired (41.5% v. 24.2%, P < 0.001), as did the emotion attribute (20.5% v. 7.7%, P < 0.001). For individuals with negative overall HUI3 scores, differences between overall HUI2 and HUI3 scores persisted after rescaling (mean difference = 0.33, P = 0.009). CONCLUSIONS: The greater range of possible scores on the HUI3, its relative ability to assess the utility of states worse than dead, and its relative superiority in discriminating moderate to severe impairment from mild or no impairment might favor its use over the HUI2 in type 2 diabetes.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".