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Record W7117778095 · doi:10.1097/aud.0000000000001779

The Relationships Between Cochlear Nerve Health and AzBio Sentence Scores in Quiet and Noise in Postlingually Deafened Adult Cochlear Implant Users

2025· article· en· W7117778095 on OpenAlexaff
Zi Gao, Yi Yuan, Jacob Oleson, Christopher R. Mueller, Ian C. Bruce, René H. Gifford, Shuman He

Bibliographic record

VenueEar and Hearing · 2025
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsMcMaster University
FundersNational Institute on Deafness and Other Communication Disorders
KeywordsQUIETCochlear implantSpeech perceptionNoise (video)Active listeningSentence

Abstract

fetched live from OpenAlex

OBJECTIVES: This study investigated the relationships between the cochlear nerve (CN) health and sentence-level speech perception outcomes measured in quiet and noise in postlingually deafened adult cochlear implant (CI) users. DESIGN: Study participants included 28 postlingually deafened adult CI users with a Cochlear Nucleus device. For each participant, only one ear was tested. Neural health of the CN was assessed at three or four electrode locations across the electrode array using two parameters derived from results of the electrically evoked compound action potential (eCAP). One parameter was the phase locking value (PLV), which estimated neural synchrony in the CN. The other parameter was the sensitivity of the eCAP amplitude growth function (AGF) slope to changes in the interphase gap (IPG) of biphasic electrical pulses (i.e., the IPGE slope ). Speech perception was tested using AzBio sentences in both quiet and a 10-talker babble background noise with +5 dB and +10 dB signal to noise ratios (SNR). IPGE slope and PLV values were averaged across electrodes for each subject, both with and without weighting by the frequency importance function (FIF) of the AzBio sentences. Pearson and Spearman correlations were used to assess the pairwise relationships between the IPGE slope , the PLV, and age. Multiple linear regression models with AzBio score as the outcome and the PLV and the IPGE slope as predictors were used to evaluate the associations between the three variables while controlling for age. RESULTS: The IPGE slope and the PLV demonstrated different patterns with regard to their relationships with electrode location, age, and speech perception. The PLV, but not the IPGE slope , differed significantly across electrodes, where the apical electrodes had larger PLVs (better neural synchrony) than the basal electrodes. The IPGE slope , but not the PLV, was significantly correlated with participant's age, where smaller IPGE slope values (poorer spiral ganglion neuron density) were associated with more advanced age. After controlling for the effect of age, the PLV, but not the IPGE slope , was significantly associated with AzBio scores in the +5 dB SNR condition, where larger PLVs predicted better speech perception. Neither the PLV nor the IPGE slope was significantly associated with AzBio score in quiet or in the +10 dB SNR condition. The result patterns remained the same regardless of whether the mean values of the IPGE slope and the PLV were weighted by the AzBio FIF. The result patterns generally did not change with fitting methods or input/output scales of the AGF slopes. CONCLUSIONS: The IPGE slope and the PLV quantify different aspects of CN health. The positive association between the PLV and AzBio scores in the +5 dB SNR condition suggests that neural synchrony is important for speech perception in adult CI users in challenging listening conditions with a relatively high noise level. The lack of association between age and the PLV indicates that reduced neural synchrony in the CN might not be the primary factor accounting for the greater deficits in understanding speech in noise observed in older CI users, as compared with middle-aged CI users.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.407

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0000.000
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 head0.046
GPT teacher head0.318
Teacher spread0.271 · 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".

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Citations1
Published2025
Admission routes1
Has abstractyes

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