Construct Validity of the EuroQoL–5 Dimension and the Health Utilities Index in Head and Neck Cancer
Bibliographic record
Abstract
Objective The objective of this study was to evaluate the construct validity of 2 health utility instruments—the EuroQoL–5 Dimension (EQ‐5D) and the Health Utilities Index–Mark 3 (HUI‐3)—and to compare them with disease‐specific measures in patients with head and neck cancer. Study Design Prospective cross‐sectional analysis. Setting Princess Margaret Cancer Centre. Methods Patients were administered the EQ‐5D, HUI‐3, the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ‐C30) and its head and neck cancer module (EORTC QLQ‐H&N35), and the University of Washington Quality of Life Questionnaire (UWQoL). Several a priori expected relations were examined. The correlative and discriminative properties of the various instruments were examined. Results A total of 209 patients completed the 4 questionnaires. A significant ceiling effect was observed among EQ‐5D responses (23% reported a maximum score of 1). The EQ‐5D (rho = 0.79) and HUI‐3 (rho = 0.60) had a strong correlation with the social‐emotional domain of the UWQoL. The EQ‐5D had a moderate correlation with the physical domain of the UWQoL (rho = 0.42), whereas the HUI‐3 had a weak correlation (rho = 0.29). The EQ‐5D and HUI‐3 were able to distinguish among levels of health severity measured on the EORTC QLQ‐C30 though not the QLQ‐H&N35. Comparatively, the UWQoL was able to distinguish levels of disease severity on the EORTC QLQ‐C30 and QLQ‐H&N35. Conclusion The results of this study demonstrate that disease‐specific domains from head and neck quality‐of‐life instruments are not strongly correlated with the EQ‐5D and HUI‐3. Consideration should be put toward development of a disease‐specific preference‐based measure for health economic evaluation. Level of evidence 4.
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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 | 0.017 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".