Modification of Osseointegrated Device Parameters to Improve Speech in Noise and Localization Ability
Bibliographic record
Abstract
OBJECTIVE: To determine how best to modify osseointegrated (OI) devices or environmental settings to maximize hearing performance. STUDY DESIGN: Prospective cohort study. SETTING: Tertiary referral center. PATIENTS: Fourteen adults with single-sided deafness (SSD) with a minimum of 6 months OI usage and nine bilaterally normal hearing controls INTERVENTIONS: : Speech in noise (SIN) and localization ability were assessed in a multi-speaker array (R-Space) with patients repeating sentences embedded in competing noise and verbally indicating the source speaker, respectively. MAIN OUTCOME MEASURES: SIN and localization were assessed with multiple OI microphone settings-fixed-directional, omnidirectional, and adaptive-as well as an unaided (OI off) condition. Participants completed the Abbreviated Profile of Hearing Aid Benefit questionnaire. RESULTS: Localization performance remains compromised for OI users with a high number of front-back confusions, but rapid learning using the fixed-directional microphone setting improved localization of sounds on the device side despite poorer localization of sounds on the normal-hearing side. SIN performance is greatly enhanced with speech presented to the contra hearing ear rather than the OI device side. Subjective report of hearing ability is highly predictive of objective SIN measures. CONCLUSIONS: Clinicians should consider implementing a fixed-directional microphone setting for improved localization for sounds behind the OI device, but inform patients of the trade-off in performance on the normal-hearing side. For better hearing in noise, clinicians should counsel OI recipients to orient the speech signal to their normal hearing ear rather than their OI device. The background noise subscale of the abbreviated profile of hearing aid benefit (APHAB) provides a meaningful metric by which to assess SIN performance of OI device users.
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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 | 0.003 |
| 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) | 0.001 | 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".