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Hearing Aid Processing Changes Tone Burst Onset: Effect on Cortical Auditory Evoked Potentials in Individuals With Normal Audiometric Thresholds

2012· article· en· W1988264673 on OpenAlexafffund
Vijayalakshmi Easwar, Danielle Glista, David W. Purcell, Susan Scollie

Bibliographic record

VenueAmerican Journal of Audiology · 2012
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsWestern University
FundersOntario Ministry of Research and InnovationChina Academy of Engineering PhysicsOntario Innovation Trust
KeywordsAudiologyHearing aidStimulus (psychology)Tone burstTone (literature)AudiometryPure toneMedicineHearing lossPsychology

Abstract

fetched live from OpenAlex

PURPOSE: The validity of using the cortical auditory evoked potential (CAEP) as an objective measure of hearing aid outcome has been questioned in the literature due to stimulus modifications caused by hearing aid processing. This study aimed to investigate the effects of hearing aid processing on the CAEP elicited with tone bursts that may have altered onsets. METHOD: CAEPs to unprocessed and hearing aid-processed tone bursts were obtained from 16 individuals with normal audiometric thresholds when the onset time, level, and signal-to-noise ratio (SNR) were matched between the 2 conditions. Tone bursts processed by the hearing aid were recorded in an anechoic box and were presented through insert receivers. Unprocessed tone bursts were superimposed with hearing aid noise floor to match the SNR of the hearing aid-processed tone bursts. RESULTS: Shortening of rise time and overshoot at the onset of the tone burst were evident in the hearing aid-processed stimuli. Statistical analysis of data showed no significant effects of hearing aid processing on the latency or amplitude of CAEP peaks (p > .05). CONCLUSION: The changes in rise time occurring in the tone bursts due to hearing aid processing may not confound CAEP measures that are used to validate hearing aid fitting.

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)0.002
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 score0.175
Threshold uncertainty score0.548

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
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 head0.023
GPT teacher head0.312
Teacher spread0.289 · 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

Citations15
Published2012
Admission routes2
Has abstractyes

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