How Robust Is the Health Utilities Index Mark 2 Utility Function?
Bibliographic record
Abstract
PURPOSE: The utility function for the Health Utilities Index Mark 2 (HUI2) system is based on preference measurements from a random sample of parents with exclusion of inconsistent respondents. Would results without exclusions or from a different group of parents have differed? METHODS: Scores were obtained from parents of patients (n = 59) undergoing treatment for cancer. Mean scores from the 2 sets of parents were compared:parents of patients and parents from the general population. Three multiattribute utility functions were estimated. Mean scores for HUI2 states using the functions were compared. RESULTS: Most differences in mean scores between different groups were not statistically significant (P < 0.05). Differences in parameter estimates among the 3 utility functions were 0.05 or less. The exponent on the power function for the parent-of-patient group was 2.16, within 6% of that for random sample parents. The intraclass correlation between scores for 144 health states derived from the random-sample-parents and parents-of-patients functions was 0.99; the mean difference per state in scores was 0.018. CONCLUSION: The HUI2 scoring function generalizes well in that different groups of parents give similar results. The HUI2 scoring function is robust in that the functions without and with exclusions generate scores that are very close in value.
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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 | 0.046 | 0.339 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".