The effect of different utility measures on the cost‐effectiveness of bilateral cochlear implantation
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: To determine if the choice of health utility measure affects the incremental cost-utility ratio (ICUR) when assessing the cost-effectiveness of bilateral cochlear implantation (CI). STUDY DESIGN: A scenario-based estimate with three scenarios: 1) a patient with severe to profound sensorineural hearing loss with no intervention, 2) the same patient with a unilateral CI with average or better performance, and 3) the same patient with bilateral CIs with average or better performance. METHODS: One hundred and forty-two subjects comprising preimplantees (n = 30), unilateral cochlear implantees (n = 30), bilateral implantees (n = 30), and healthcare professionals (n = 52). The four health utility instruments applied were the Health Utility Index Mark 3 (HUI3), European Quality of Life Questionnaire in 5 Domains (EQ5D), visual analog scale (VAS), and time trade-off (TTO). Cost for each implant was based on a 25-year time horizon, 50% discount for the second implant, and a 15% failure rate. RESULTS: Using the HUI3, the utility gain from unilateral to bilateral implantation was 0.035 or 11.5% of the total utility gain. This ratio was higher using the other instruments: EQ5D (22.2%), VAS (35.0%), and TTO (41.4%). For the scenario of bilateral CI compared to no intervention, HUI3 ICUR estimates were the lowest, and for bilateral CI compared to unilateral CI, HUI3 ICUR estimates were the highest. CONCLUSIONS: The choice of utility instrument in cost-utility analysis of bilateral CI heavily influences whether the second implant is deemed cost-effective. The HUI3 is the utility of choice in CI studies and is the most conservative. LEVEL OF EVIDENCE: 4.
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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 | 0.034 | 0.143 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".