Responsiveness of Health State Utility Values in Knee Osteoarthritis
Bibliographic record
Abstract
OBJECTIVE: Adaptive tests are increasingly being used to assess health-related quality of life in patients with a variety of medical conditions, including osteoarthritis (OA) of the knee. This approach has recently been used to assess health state utility valuations (HSUV) for use in quality-adjusted life-year calculations. To accurately assess incremental value for money, these tools must be responsive. Therefore, we examined the responsiveness of the Health Utilities Index mark 3 (HUI3) and Paper Adaptive Test-5D (PAT-5DQOL) in a group of patients with knee OA. METHODS: We used patient-level data from a randomized controlled trial evaluating a pharmacist-initiated multidisciplinary intervention in newly diagnosed patients with knee OA. The mean change for utility scores from baseline to 6 months was calculated, as well as effect size (ES) and standardized response mean (SRM) for the HUI3 and PAT-5DQOL, and generalized additive model plots, using the Western Ontario and McMaster Osteoarthritis index as a reference standard. RESULTS: When patients were assessed based on whether their condition had improved, remained unchanged, or worsened over time, the PAT-5DQOL showed greater responsiveness in patients whose condition had either improved or worsened. ES and SRM were generally small for both instruments. CONCLUSION: The PAT-5DQOL is more responsive to change over time than the HUI3 in patients with knee OA.
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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 | 0.062 | 0.215 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".