Output Vibration Measurements of Bone-Anchored Hearing Aids
Bibliographic record
Abstract
HYPOTHESIS: Different bone-anchored hearing aids (BAHAs) processors have different output vibration characteristics, which depend on the mechanical load and the volume setting. Responses will differ between live heads and dry or plastic skulls. BACKGROUND: The BAHA is an implantable bone-conduction device. Three different BAHA models are available. Their output vibrations have not been reported using a noncontact method with differing impedance loads, including the BAHA-fitted patient head. METHODS: Using a laser-Doppler vibrometer, vibration responses with sound input of 70- to 80-dB sound pressure level were measured on unloaded BAHAs, a dry skull, a plastic skull, and on the abutments of three live BAHA-fitted patients. Responses at different volume settings and distances from the vibrator were also tested. Frequency responses were calculated for displacement, velocity, and acceleration. RESULTS: Unloaded BAHA accelerations were approximately 30 to 50 dB higher than live-head accelerations. Live-head accelerations were similar to dry skulls in frequencies of more than 500 Hz, but much higher than the plastic skull responses. Live-head responses were more damped. The Cordelle II outperformed the other two processors by approximately 20 dB. The Classic 300 had better low-frequency responses than the Compact. The volume settings had little effect on vibration output overall. Acceleration peak was at approximately 2.5 kHz for all conditions. CONCLUSION: The BAHA processors differ in the output acceleration they can achieve with differing loads. The volume control setting has little impact on accelerations produced for most processors. The live-head responses are similar to the dry skull in frequencies of more than 500 Hz.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.003 |
| 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) | 0.003 | 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".