Development, Validation, and Valuation of a Head and Neck Cancer−Specific Health Utility Instrument (HNC-8D)
Bibliographic record
Abstract
Importance: Generic health utility instruments lack the discriminative ability to differentiate among health states in patients after head and neck cancer treatment. Objective: To develop, validate, and valuate a head and neck cancer-specific health utility measure. Design, Setting, and Participants: This psychometric study comprised 2 phases to develop and validate a health utility instrument. The first phase, development and validation, occurred from January 2021 to August 2022. An expert panel selected disease-specific quality-of-life instruments as the basis for a new utility instrument. Two datasets (n = 458 and 493) were used to establish dimension structure through exploratory factor analysis, and to select items using Rasch and psychometric criteria and expert opinion. Discriminative validity of the new instrument was tested by comparing scores for different disease severities (patients with and without gastrostomy and tracheostomy tubes). The second phase, valuation, was conducted from January 2023 to January 2024 in a quaternary referral center with healthy participants. Participants completed time-trade-off exercises for 100 sampled health states and were randomized to discovery and validation sets (80:20). Using a repeated measures model, a scoring algorithm to predict utilities of health states within the instrument was created in the discovery set and tested in both sets. Data were analyzed from January 2022 to December 2023. Intervention: Participants performed time-trade-off exercises for various states. Main Outcomes and Measures: Discriminative validity (first phase) and the mean absolute differences of predicted and observed utilities (second phase). Results: The European Organization for Research and Treatment of Cancer's Quality of Life Questionnaire-Core 30 and its Head and Neck module 43 were selected by the expert panel and used as the basis instruments. Exploratory factor analysis established 8 dimensions, with 1 item selected per dimension. Of the 488 respondents, 84 with gastrostomy and/or tracheostomy tubes reported lower scores for 7 of the 8 items. In the second phase, 2497 valuations were performed by 250 healthy participants (mean [SD] age, 42.4 [16.5] years; 166 [66%] females). The scoring algorithm produced mean absolute differences between predicted and observed utilities of 0.041 (95% CI, 0.034-0.047) and 0.082 (95% CI, 0.065-0.100) in the discovery and validation sets, respectively. Conclusions and Relevance: This psychometric study developed a new head and neck cancer-specific utility measure, the HNC-8D (Head and Neck Cancer-8 Dimensions). The instrument demonstrated predictive accuracy for measuring health utility and can be used to differentiate health utility states following head and neck cancer treatment.
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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 | 0.045 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".