DSL prescriptive targets for bone conduction devices: adaptation and comparison to clinical fittings
Bibliographic record
Abstract
OBJECTIVE: To develop an algorithm that prescribes targets for bone conduction frequency response shape, compression, and output limiting, along with a clinical method that ensures accurate transforms between assessment and verification stages of the clinical workflow. DESIGN: Technical report of target generation and validation. STUDY SAMPLE: We recruited 39 adult users of unilateral percutaneous bone conduction hearing aids with a range of unilateral, bilateral, mixed and conductive hearing losses across the sample. RESULTS: The initial algorithm over-prescribed output compared to the user's own settings in the low frequencies, but provided a good match to user settings in the high frequencies. Corrections to the targets were derived and implemented as a low-frequency cut aimed at improving acceptance of the wearer's own voice during device use. CONCLUSIONS: The DSL-BCD prescriptive algorithm is compatible with verification of devices and fine-tuning to target for percutaneous bone conduction hearing devices that can be coupled to a skull simulator. Further study is needed to investigate the appropriateness of this prescriptive algorithm for other input levels, and for other clinical populations including those with single-sided deafness, bilateral devices, children and users of transcutaneous bone conduction hearing aids.
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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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".