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Record W2156014822 · doi:10.1155/2012/982894

A Pilot Study on Cortical Auditory Evoked Potentials in Children: Aided CAEPs Reflect Improved High-Frequency Audibility with Frequency Compression Hearing Aid Technology

2012· article· en· W2156014822 on OpenAlexafffund
Danielle Glista, Vijayalakshmi Easwar, David W. Purcell, Susan Scollie

Bibliographic record

VenueInternational Journal of Otolaryngology · 2012
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsWestern University
FundersCanadian Institutes of Health ResearchPhonak
KeywordsAudiologyMedicineHearing aidCompression (physics)

Abstract

fetched live from OpenAlex

Background. This study investigated whether cortical auditory evoked potentials (CAEPs) could reliably be recorded and interpreted using clinical testing equipment, to assess the effects of hearing aid technology on the CAEP. Methods. Fifteen normal hearing (NH) and five hearing impaired (HI) children were included in the study. NH children were tested unaided; HI children were tested while wearing hearing aids. CAEPs were evoked with tone bursts presented at a suprathreshold level. Presence/absence of CAEPs was established based on agreement between two independent raters. Results. Present waveforms were interpreted for most NH listeners and all HI listeners, when stimuli were measured to be at an audible level. The younger NH children were found to have significantly different waveform morphology, compared to the older children, with grand averaged waveforms differing in the later part of the time window (the N2 response). Results suggest that in some children, frequency compression hearing aid processing improved audibility of specific frequencies, leading to increased rates of detectable cortical responses in HI children. Conclusions. These findings provide support for the use of CAEPs in measuring hearing aid benefit. Further research is needed to validate aided results across a larger group of HI participants and with speech-based stimuli.

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)0.001
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 score0.680
Threshold uncertainty score0.751

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.040
GPT teacher head0.321
Teacher spread0.281 · 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

Citations45
Published2012
Admission routes2
Has abstractyes

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