Validation of the Hearing Utility Measure (HUM) in Cochlear Implant Candidates and Users
Bibliographic record
Abstract
OBJECTIVE: To validate the Hearing Utility Measure (HUM), a tool designed to quantify hearing loss impact on health and quality of life for use in economic evaluation. STUDY DESIGN: Prospective cohort. SETTING: Tertiary academic center. METHODS: Cochlear implant (CI) candidates were enrolled between June 25, 2020, and March 17, 2024. Participants completed HUM alongside other assessments, including Health Utilities Index, Mark-3 (HUI-3), Speech, Spatial and Qualities of Hearing Scale (SSQ-49), Cochlear Implant Quality of Life Profile (CIQOL-35), and Tinnitus Handicap Inventory (THI) at the time of CI evaluation and 12 months post-activation. A subgroup with stable hearing status completed HUM twice, 6 months apart, to assess test-retest reliability. RESULTS: Among 1315 participants, HUM showed strong correlations with CIQOL-global (r = 0.69; 95% CI: 0.66-0.72) and SSQ-overall (r = 0.70; 95% CI: 0.67-0.73), supporting construct validity. HUM Tinnitus domain correlated negatively with THI (-0.75; -0.77, -0.73), as expected. Test-retest reliability was excellent (Intraclass Correlation Coefficient [ICC] 0.90, 95% CI: 0.83-0.94). HUM hearing utility was higher for candidates with single-sided deafness (SSD) (0.76) than with traditional and asymmetric hearing loss (0.58, P < .001). Median HUM improved from 0.70 to 0.78 for SSD recipients (P = .002) and 0.57 to 0.75 for traditional recipients (P < .001). CONCLUSION: HUM is a reliable, valid, and responsive measure of hearing-related health in adults with advanced hearing loss. Unlike traditional instruments such as SSQ and CIQOL, which capture patient-reported outcomes but are not designed for economic analysis, HUM is a preference-based utility instrument that enables cost-effectiveness evaluation. It outperforms the available HUI-3 by better reflecting hearing loss impact and complements existing nonutility instruments used in clinical and health policy decision-making.
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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 | 0.012 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".