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Record W4220919693 · doi:10.3390/audiolres12020019

Analysis of the Acoustic Transcranial Bone Conduction

2022· article· en· W4220919693 on OpenAlexaff
Catherine Dufour-Fournier, Arnaud Devèze, Jonathan Barbut, Erick Ogam, Issam Saliba, Catherine Masson

Bibliographic record

VenueAudiology Research · 2022
Typearticle
Languageen
FieldMedicine
TopicEar Surgery and Otitis Media
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsBone conductionAcousticsCochleaSound transmission classSkullMaterials scienceAudiologyAnatomyMedicinePhysics

Abstract

fetched live from OpenAlex

OBJECTIVES: (1) To analyze the preferential pathways of sound transmission and sound waves travelling properties in the skull and (2) to identify the location(s) on the skull where bone conduction to the cochlea is optimal. STUDY DESIGN: Basic research Methods: Nine cadaveric heads were placed in an anechoic chamber and equipped with six Bone Anchored Hearing Aids (BAHA™) implants (Cochlear™, Sydney, NSW, Australia) and fifteen accelerometers. A laser velocimeter was used to measure cochlear response by placing a reflector on the round window. Different frequency sweeps were applied to each implant, and measurements were recorded simultaneously by the laser velocimeter and accelerometers. RESULTS: Low-frequency sound waves mostly travel the frontal transmission pathways, and there is no clear predominant pattern for the high frequencies. The mean inter-aural time lag is 0.1 ms. Optimal sound transmission to the cochlea occurs between 1000 and 2500 Hz with a contralateral 5 to 10 dB attenuation. The implant location does not influence mean transmission to the cochlea. CONCLUSION: There is a pattern of transmission for low frequencies through a frontal pathway but none for high frequencies. We were also able to demonstrate that the localization of the BAHA™ implant on the skull had no significant impact on the sound transmission, either ipsi or contralaterally.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.278
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.096
GPT teacher head0.387
Teacher spread0.291 · 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.

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

Citations2
Published2022
Admission routes1
Has abstractyes

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