A model to estimate health utilities index mark 3 utility scores from WOMAC index scores in patients with osteoarthritis of the knee.
Bibliographic record
Abstract
OBJECTIVE: To develop a formula to translate Western Ontario and McMaster University Osteoarthritis Index (WOMAC) scores collected in clinical trials of patients with osteoarthritis (OA) into Health Utilities Index Mark 3 (HUI3) utility scores for application in economic evaluation. METHODS: Data from a previously published open-label randomized controlled trial of appropriate care with hylan G-F 20 versus appropriate care without hylan G-F 20 in 255 outpatients with knee OA. We estimated linear regression models of HUI3 scores using various functions of WOMAC, demographics, and clinical variables. Out-of-sample predictive performance of the models was assessed using the mean absolute error and several other criteria. RESULTS: The preferred formula included WOMAC pain, stiffness, function subscales, demographic variables; it accounted for almost 40% of the variation in the HUI3 utility scores. At the group level, absolute differences between predicted and actual overall HUI3 utility scores were < 0.001 and not statistically significantly different from zero. CONCLUSION: A formula was derived from the WOMAC index to estimate overall utility scores based on the HUI3 for studies of patients with OA for whom utility has not been recorded. Researchers can estimate overall utility scores, compute quality-adjusted life-years, and perform cost-utility analyses within a defined range of certainty.
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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 | 0.015 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".