The Effect of Angulation of the Vibrating Floating Mass Transducer on Stapes Velocity
Bibliographic record
Abstract
HYPOTHESIS: Changes to the angular position of the vibrating floating mass transducer (FMT) coupled to the long process of the incus will not affect stapes velocity. OBJECTIVE: The MED-EL Vibrant Soundbridge is an active middle ear implantable device, which constitutes an effective alternative to acoustic hearing aids for the rehabilitation of patients with sensorineural and mixed hearing loss. Because of varied anatomy, it is not always possible to position the FMT in line with the vibrating axis of the stapes. Changes in stapes velocity after angulation of the FMT are measured using laser Doppler vibrometry (LDV). METHODS: The study was performed on 7 human cadaveric temporal bones. The FMT was attached to the incus and angled at the recommended 0 degree or at 45 degrees relative to the vibrating axis of the stapes, and the stapes velocity measured using LDV. RESULTS: In comparison to the 0-degree position, angulating the FMT to 45 degrees reduced cochlea input as measured by stapes velocity, although there was no statistical significance to this difference. Placing the FMT at 45 degrees did not compromise the peak output of the device but resulted in a phase lag which was more marked compared with the 0-degree position. CONCLUSION: If it is not anatomically possible to position the FMT in line with the vibrating axis of the stapes, then placement at up to 45 degrees does not significantly alter the performance of the implant particularly in the midfrequencies that are crucial to the understanding of speech.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".