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Record W4214821888 · doi:10.1080/14992027.2022.2041739

A novel method for objective in-situ measurement of audibility in bone conduction hearing devices – a pilot study using a skin drive BCD

2022· article· en· W4214821888 on OpenAlexaff
Ann-Charlotte Persson, Bo Håkansson, Moksha Caveramma Mechanda, William Hodgetts, Karl-Johan Fredén Jansson, Måns Eeg‐Olofsson, Sabine Reinfeldt

Bibliographic record

VenueInternational Journal of Audiology · 2022
Typearticle
Languageen
FieldMedicine
TopicEar Surgery and Otitis Media
Canadian institutionsUniversity of Alberta
FundersStiftelsen Tysta Skolan
KeywordsHearing aidBone conductionMicrophoneAcousticsAudiologyComputer scienceMedicineLoudspeakerPhysics

Abstract

fetched live from OpenAlex

OBJECTIVE: Objective measurement of audibility (verification) using bone conduction devices (BCDs) has long remained an elusive problem for BCDs. For air conduction hearing aids there are well-defined and often used objective methods, and the aim of this study is to develop an objective method for BCDs. DESIGN: In a novel setup for audibility measurements of bone-anchored hearing aid (BAHA) attached via a soft band, we used a skin microphone (SM) on the forehead measuring in-situ sound field thresholds, maximum power output (MPO) and international speech test signal (ISTS) responses. STUDY SAMPLE: Five normal-hearing persons. RESULT: Using the electrical output of SM it was possible to objectively measure the audibility of a skin drive BCD, presented as an eSPL-o-gram showing thresholds, MPO and ISTS response. Normalised eSPL-o-gram was verified against corresponding FL-o-grams (corresponding force levels from skull simulator and artificial mastoid (AM)). CONCLUSION: The proposed method with the SM can be used for objective measurements of the audibility of any BCDs based on thresholds, MPO and speech response allowing for direct comparisons of hearing and BCD output on the same graph using an eSPL-o-gram. After normalisation to hearing thresholds, the audibility can be assessed without the need for complicated calibration procedures.

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)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.411
Threshold uncertainty score0.312

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.134
GPT teacher head0.390
Teacher spread0.256 · 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

Citations11
Published2022
Admission routes1
Has abstractyes

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