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Record W2066977282 · doi:10.1097/aud.0b013e3181f0b685

Technology-Limited and Patient-Derived Versus Audibility-Derived Fittings in Bone-Anchored Hearing Aid Users: A Validation Study

2010· article· en· W2066977282 on OpenAlexaff
William Hodgetts, Paul Hagler, Bo Håkansson, Sigfrid D. Soli

Bibliographic record

VenueEar and Hearing · 2010
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsUniversity of Alberta HospitalUniversity of Alberta
Fundersnot available
KeywordsLoudnessHearing aidSound qualityAudiologyNoise (video)Speech perceptionQUIETConsonantComputer scienceSpeech recognitionPerceptionPsychologyMedicineArtificial intelligence

Abstract

fetched live from OpenAlex

In Brief Objectives: Current approaches to fit bone-anchored hearing aid (Baha) rely heavily on patient feedback of “loudness” and “sound quality.” Audiologists are limited to this approach for two reasons: (1) the technology in current models of Baha does not allow for much fine-tuning of frequency response or maximum output on an individual basis and (2) there has not been a valid approach to verify the frequency response or maximum output on an individual basis. The objectives of this study are to (1) describe an alternative approach to fit Baha, an “audibility-derived (AD)” fitting, and (2) test whether outcomes improve with this new fitting compared with the current “patient-derived (PD)” fitting. Design: This study used a repeated measures design where each subject experienced both the AD and PD fittings in random order. Subjects were tested on a variety of outcome measures including output levels of aided speech, hearing in noise test (quiet and in noise), consonant recognition in noise, aided loudness, and subjective percentage of words understood. Results: Electromechanical testing revealed significantly higher aided output with the AD fitting, especially in the high frequencies. Subjects performed significantly better in all outcome measures with the AD fitting approach except when testing aided loudness and subjective perception for which the differences were nonsignificant. When the input levels to the Baha were soft, advantages for the AD fitting were emerging on these tests, but they did not reach significance. Conclusions: This study presents a more objective, fitting approach for Baha that leads to better outcomes in the laboratory. The next steps will be to test these fittings in the real world and to make the approach generally available to clinicians fitting Bahas. The majority of bone-anchored hearing aid (Baha) fittings consist of estimating the proper device from the standard bone conduction audiogram and then obtaining the patient's feedback on loudness and sound quality of the aid, with the occasional trim pot adjustment to tweak the settings. This article proposes an alternative approach to fit Baha that mirrors modern approaches to prescribe air conduction hearing aids and then compares the outcomes of these two approaches on patients with Baha.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.714
Threshold uncertainty score0.632

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.043
GPT teacher head0.296
Teacher spread0.253 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations17
Published2010
Admission routes1
Has abstractyes

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