Mapping the University of Washington Quality of life questionnaire onto EQ‐5D and HUI‐3 indices in patients with head and neck cancer
Bibliographic record
Abstract
BACKGROUND: There is no mechanism to predict health utility (HU) values from the University of Washington Quality of Life Questionnaire (UWQoL) scores. We sought to develop a mapping algorithm capable of using UWQoL data to approximate HU scores. METHODS: Outpatients with head and neck cancer completed the UWQoL, EQ-5D, and the Health Utilities Index-Mark 3 (HUI-3). Results of the UWQoL were mapped onto both EQ-5D and HUI-3 scores using ordinary least-squares regression models. Two-part models were explored. The predictive power of the model was assessed using 10-fold cross-validation. RESULTS: = 0.628, root mean square error = 0.076). Both models demonstrated construct validity by discriminating between clinical indices of disease severity. CONCLUSIONS: The abovementioned algorithms enable researchers to perform health economic evaluations with existing UWQoL data in cases where prospectively collected HU values are not available.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".