Bibliographic record
Abstract
BACKGROUND: The Health Utilities Index Mark 3 (HUI3) is a comprehensive, compact health status classification and health state preference system. The HUI3 system has been included in 4 Canadian population health surveys and numerous clinical trials. OBJECTIVES: To evaluate the construct validity of the HUI3 for the measurement of health-related quality of life (HRQL) and attribute-specific morbidity in respondents to the 1990 Ontario Health Survey reported to have arthritis or stroke. The authors assessed (1) whether those with stroke, arthritis, and both conditions had lower HRQL scores than those with neither condition and (2) whether HUI3 detects morbidity in specific health attributes affected by arthritis and stroke. Stroke (but not arthritis) were expected to affect speech and cognition; arthritis (but not stroke) to affect pain; both to affect mobility, dexterity, and emotion; and neither to affect vision and hearing. RESEARCH DESIGN: Linear regression models of HRQL and attribute-specific utilities were estimated as a function of 3 indicator variables of health problem (stroke only, arthritis only, both) and variables included to reduce confounding. RESULTS: Subjects with stroke, arthritis, and both conditions had substantially lower HRQL than those with neither condition. Stroke subjects had greater morbidity in speech and cognition than arthritis subjects; somewhat surprisingly, pain morbidity was only slightly higher among arthritis subjects; neither condition affected vision or hearing. These associations were robust to various model specifications. CONCLUSIONS: The HUI3 system appears valid for measuring health status and HRQL for stroke and arthritis in the context of a noninstitutionalized population health survey.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.117 | 0.037 |
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".