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Record W7117107522 · doi:10.3390/audiolres16010006

Questioning the Usefulness of Stimulation Rate Changes to Optimize Perception in Cochlear Implant Users

2025· article· en· W7117107522 on OpenAlexafffund
Andréanne Sharp, Daniel Beaudoin, Julie Dufour, Benoit-Antoine Bacon, François Champoux

Bibliographic record

VenueAudiology Research · 2025
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsUniversity of British ColumbiaUniversité de MontréalCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-MontréalUniversité Laval
FundersFonds de Recherche du Québec - SantéNatural Sciences and Engineering Research Council of CanadaUniversité de Montréal
KeywordsCochlear implantPerceptionInteroperabilityObservational studyIntervention (counseling)Speech perceptionMEDLINE

Abstract

fetched live from OpenAlex

Research exploring the impact of stimulation rate modifications on perception in cochlear implant users continues to expand. The existing body of research remains contradictory, making it difficult to establish a clear consensus that could inform clinical recommendations. In this context, this article aims to question the usefulness of such adjustments as a clinical intervention beyond the initial fitting, particularly for optimizing non-speech processing. To do so, we combined an overview of the existing literature on the effects of stimulation-rate changes on speech and non-speech processing with a discussion of observational data. The current evidence base is sparse, often contradictory, and affected by interoperability challenges that limit cross-study comparability. Consequently, it is not possible to formulate robust, evidence-based clinical recommendations at this time. Clinicians should be cautious about implementing stimulation-rate adjustments beyond the initial fitting and should wait for more robust evidence to emerge before considering such changes.

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)0.004
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 score0.954
Threshold uncertainty score0.429

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.151
GPT teacher head0.426
Teacher spread0.275 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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