Clinical evaluation of DSL-BCD fittings: assessing output-to-targets for percutaneous bone conduction devices in practice
Bibliographic record
Abstract
Objectives Objective measurements and prescriptive formulas for air-conduction hearing aids have been discussed extensively in the literature. In contrast, the application of objective verification tools such as clinical skull-simulators in the bone-conduction amplification literature is still emerging. This approach offers a critical step toward understanding the variability in percutaneous bone-conduction hearing devices (BCD) output on a larger scale. Our study aims to quantify this variability using skull-simulator measurements of percutaneous BCD users, thereby generating accuracy description of the range of outputs normally fitted in a clinical context. Design A cross-sectional retrospective study was conducted, analyzing skull-simulator measurements from routine audiology appointments of 79 adult percutaneous BCD users fitted with the DSL-BCD v1.1 prescription procedure. We explored the relationship between in-situ bone-conduction hearing thresholds and frequency-specific and root mean square error (RMSE) deviations from targets, along with aided speech intelligibility index (SII) using linear regressions for standard speech signals. BCD skull-simulator measurements were conducted at the user's daily listening level. Results At a 65 dB SPL speech input, over 50% of participants showed output-to-target deviations within ±5 dB at all frequencies except 750, 6,000, and 8,000 Hz. Fittings generally fell below targets as in-situ bone-conduction thresholds worsened, with exceptions at 250 and 500 Hz showing the reverse trend. A significant correlation was observed between in-situ bone-conduction thresholds and aided SII across all input levels, indicating an expected decrease in aided SII with increased bone-conduction hearing loss. The RMSE was within 5 dB for 37% of participants, lower than reported in air-conduction hearing aid literature. Secondary analyses assessing BCD capabilities of matching targets following fine-tuning, without participants' involvement, indicated that a RMSE within 5 dB could be met in 79% of cases. Conclusions This study outlines the deviations in output-to-target and aided SII among adult percutaneous BCD users, offering clinicians a benchmark through 95% confidence interval regression models and in-situ threshold averages. This normative data is beneficial for fitting BCDs in users with elevated in-situ thresholds, reducing uncertainty about optimal fittings. Further research is necessary to elucidate the relationship between aided SII, output-to-target deviations, outcomes and preferences in percutaneous BCD wearers.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".