Test–retest reliability of health utilities index scores: Evidence from hip fracture
Bibliographic record
Abstract
OBJECTIVES: There is relatively little evidence on the test-retest reliability of utility scores derived from multiattribute measures. The objective was to estimate test-retest reliability for Health Utilities Index Mark 2 (HUI2) and Mark 3 (HUI3) utility scores in patients recovering from hip fracture. METHODS: We enrolled an inception cohort of hip fracture patients within 3 to 5 days of surgery. Baseline assessments included the Functional Independence Measure (FIM), Folstein Mini-Mental State Examinations, and the HUI2 and HUI3 questionnaire. Follow-up assessments at 1, 3, and 6 months also included a global change question. Test-retest reliability was assessed as agreement between 3- and 6-month scores using the intraclass correlation coefficient (ICC). Two approaches were used to classify patients as stable; a third approach based on the generalizability theory was also used. Patients were classified as stable if their FIM overall scores changed by 10 points or fewer and if they classified themselves as having experienced no or only a little change according to their global change question. RESULTS: Complete data at both the 3- and 6-month assessments based on self-report were available for 196 patients; 141 patients with complete data were classified as stable. The ICCs for HUI2 and HUI3 for stable patients were 0.71 and 0.72; the ICCs derived from the generalizability theory were 0.76 and 0.77. CONCLUSIONS: Test-retest reliability for HUI in this cohort was similar to reliability estimates for other preference-based multiattribute and generic health-profile measures--in the acceptable range for making valid group-level comparisons.
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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 | 0.056 | 0.200 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".