Examining Force Level Output of Skin-Drive Bone Conduction Hearing Devices in Adults With Simulated Conductive Hearing Loss
Bibliographic record
Abstract
PURPOSE: Bone conduction hearing devices (BCDs) that deliver sound across the skin (i.e., transcutaneous) are suitable for some individuals who have conductive or mixed hearing losses. Prescriptive targets for percutaneous devices are available, for example, from the Desired Sensation Level-Bone Conduction Hearing Device (DSL-BCD) algorithm. These targets, however, may require modification for use with transcutaneous BCDs. The current study investigated three key variables that may inform target modification: (a) comparison of thresholds measured using an audiometric bone conduction (BC) transducer versus transcutaneous BCDs that offer in situ threshold measurement, (b) transcutaneous BCD default force level outputs versus recommended DSL percutaneous BC targets, and (c) the preferred listening levels (PLLs) of adults wearing transcutaneous BCDs in a laboratory setting. METHOD: Bilateral conductive hearing loss was simulated in 20 normal-hearing adults via earplugs. Thresholds were measured using a B-71 BC transducer and two commercially available BCDs coupled to a soft headband. DSL percutaneous BC targets were generated, and PLLs were obtained for a 60-dB SPL speech stimulus. Force level outputs were measured using a skull simulator on the Audioscan Verifit2 at the hearing aids' default settings and at the participants' PLL for each device. RESULTS: On average, audiometric BC thresholds were significantly better than those measured in situ with each BCD. PLLs were similar to prescribed targets for one device with the smoother response shape and agreed in the high frequencies for both devices. CONCLUSIONS: In situ thresholds are significantly higher than audiometric BC thresholds, suggesting that device-based in situ measurement more accurately accounts for the signal transmission from transcutaneous BCDs. PLLs differed from the percutaneous targets and varied between devices, which may indicate that either target modifications or manipulations of device frequency response shaping are needed to approximate PLL with transcutaneous BCD devices.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".