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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".