The Potential Added Value of Novel Hearing Therapeutics: An Early Health Economic Model for Hearing Loss
Bibliographic record
Abstract
OBJECTIVE: To construct an early health economic model to assess the potential added value of novel hearing therapeutics, compared with the current standard of care. We use idiopathic sudden sensorineural hearing loss (ISSNHL) as a case example, because it is a lead indication for several emerging hearing therapeutics. METHODS: A decision analytic model was developed to assess the costs and effects of using novel hearing therapeutics for patients with ISSNHL. This was compared to the current standard of care. Input data were derived from literature searches and expert opinion. The study adopted a healthcare perspective of the UK National Health Service. Four analyses were conducted: 1) headroom, 2) scenario, 3) threshold, 4) sensitivity. RESULTS: The decision analytic model showed that novel therapeutics for ISSNHL have potential value both in terms of improved patient outcomes, as well as incremental net monetary benefit (iNMB). The base case analysis revealed an iNMB of £39,032 for novel therapeutics compared with the current standard of care. Results of the threshold and scenario analysis revealed that age of treatment and severity of ISSNHL are major determinants of iNMB for novel therapeutics. CONCLUSION: This article describes the first health economic model for novel therapeutics for hearing loss; and shows that novel hearing therapeutics can be cost-effective under NICE's cost-effectiveness threshold, with considerable room for improvement in the current standard of care. Our model can be used to inform the development of cost-effective hearing therapeutics; and help decision makers decide which therapeutics represent value for money.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".