Derivation and Initial Validation of the Utility Function for the Hearing Utility Measure (<scp>HUM</scp>)
Bibliographic record
Abstract
OBJECTIVE: The Hearing Utility Measure (HUM) is a replacement hearing attribute for the Health Utilities Index, Mark 3 (HUI-3) designed to improve the responsiveness of utility estimates to changes in hearing-related quality of life. The final development step is to derive the instrument's utility scoring function. METHODS: Residents of Ontario, Canada, aged ≥18 years participated in standard gamble and visual analogue scale exercises. Valuations for levels (response options) within each domain, and for each domain relative to the other domains were elicited and used to generate a hearing utility function. The function outputs hearing utility ranging from 0 = 'unable to hear at all' to 1 = 'perfect hearing' for each of the 25,920 hearing states classifiable by the HUM. Performance was assessed relative to the criterion standard: directly elicited standard gamble utility. Distributions of HUM-derived hearing utility were compared with legacy HUI-3 derived estimates. RESULTS: A total of 126 respondents participated (mean age 39.2, range 18-85 years, 53% female [67/126]). The utility function performed well in the estimation of directly elicited utilities (mean difference 0.03, RMSE 0.06). Using the legacy HUI-3, estimated hearing utility was 1.0 for 118/126 respondents (93.6%) compared with just 66/126 (52.4%) using the HUM. CONCLUSION: The new hearing attribute is capable of measuring variations in hearing utility not captured by the legacy HUI-3, especially near the ceiling of hearing function. These findings justify its application and further work to study its measurement properties in hearing loss populations. LEVEL OF EVIDENCE: 3 Laryngoscope, 134:4754-4762, 2024.
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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 | 0.020 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".