COMPARING COMMUNITY-PREFERENCE–BASED AND DIRECT STANDARD GAMBLE UTILITY SCORES: EVIDENCE FROM ELECTIVE TOTAL HIP ARTHROPLASTY
Bibliographic record
Abstract
OBJECTIVES: Do utility scores based on patient preferences and scores based on community preferences agree? The purpose is to assess agreement between directly measured standard gamble (SG) utility scores and utility scores from the Health Utilities Index Mark 2 (HUI2) and Mark 3 (HUI3) systems. METHODS: Patients were assessed repeatedly throughout the process of waiting to see a surgeon, waiting for surgery, and recovery after total hip arthroplasty (THA). Group mean scores are compared using paired t-tests. Agreement is assessed using the intraclass correlation coefficient (ICC). RESULTS: The mean SG, HUI2, and HUI3 (SD) scores at assessment 1 are 0.62 (0.31), 0.62 (0.19), and 0.52 (0.21); n=103. At assessment 2, the means are 0.67 (0.30), 0.68 (0.30), and 0.58 (0.22); n=84. There are no statistically significant differences between group mean SG and HUI2 scores. Mean SG and HUI3 scores are significantly different. ICCs are low. CONCLUSIONS: At the mean level for the group, SG and HUI2 scores match closely. At the individual level, agreement is poor. HUI2 scores were greater than HUI3 scores. HUI2 and HUI3 are appropriate for group level analyses relying on community preferences but are not a good substitute for directly measured utility scores at the individual level.
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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 | 0.034 | 0.171 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".