Improving the Hearing Status Discrimination of the Health Utilities Index, Mark 3: Design of the Hearing Status Classification System
Bibliographic record
Abstract
OBJECTIVE: Redesign the health status classification system of the Health Utilities Index, Mark 3 (HUI-3) "hearing" attribute to improve its discrimination of hearing-impaired health states. STUDY DESIGN: Domain and item selection from a previously generated item set guided by a domain and item importance survey, structural independence, and cognitive interviews with patients. SETTING: Tertiary referral center. PARTICIPANTS: Patients with a range of hearing loss severities, etiologies, and treatment experiences participated in the domain and item importance survey (n = 108) and hour-long cognitive interviews (n = 10). MAIN OUTCOME AND MEASURES: Subattributes and levels for the novel Hearing attribute. Domain and item importance was scored on a seven-point Likert scale (1, not at all important; 7, extremely important). RESULTS: Mean domain importance was highest for "speech in noise" (6.21; 95% confidence interval, 5.98-6.43) and lowest for "benefits of hearing loss" (3.46; 95% confidence interval, 3.03-3.89). Domains with moderate or greater ( r ≥ 0.5) domain importance Pearson correlation or construct overlap that interfered with structural independence were combined into single subattributes. Iterative adjustments to instructions, items, and phrasing were guided by cognitive interviews to derive the final instrument with seven subattributes: speech, environmental sounds, localization, listening effort, tinnitus, music, and assistive devices. The novel hearing attribute defines 25,920 unique hearing states. CONCLUSION: The novel HUI-hearing is part of a comprehensive health utility instrument designed for individuals with hearing loss. Pending derivation of a hearing single attribute utility function and evaluation of measurement properties, our innovative approach could be used to improve health utility measurement for impairments described by any of the other HUI-3 attributes.
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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 | 0.022 | 0.044 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".