Direct bone conduction stimulation: Ipsilateral effect of different transducer attachments in active transcutaneous devices
Bibliographic record
Abstract
Active transcutaneous bone conduction devices, where the transducer is implanted, are used for rehabilitation of hearing impaired patients by directly stimulating the skull bone. The transducer and the way it is attached to the bone play a central role in the design of such devices. The actual effect of varying the contact to bone has not been addressed yet. The aim of this study is therefore to compare how different attachment methods of the transducer to the bone for direct stimulation affect the ear canal sound pressure and vibration transmission to the ipsilateral cochlea. Three different attachments to the bone were tested: (A) via a flat small-sized surface, (B) via a flat wide surface and (C) via two separated screws. Measurements were done on four human heads on both sides. The attachments were compared in terms of induced cochlear promontory velocity, measured by a laser Doppler vibrometer, and ear canal sound pressure, measured by a low noise microphone. A swept sine stimulus was used in the frequency range 0.1-10 kHz. On an average level, the attachment method seems to affect the transmission mainly at frequencies above 5 kHz. Furthermore, the results suggest that a smaller contact surface might perform better in terms of transmission of vibrations at mid and high frequencies. However, when considering the whole frequency range, average results from the different attachment techniques are comparable.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.002 | 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".