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Record W4417497784 · doi:10.1080/14992027.2025.2601648

Cochlear implant wear time and speech perception outcomes in adults: recommendation for minimum daily wear time

2025· article· en· W4417497784 on OpenAlexaff
Jacob Sulkers, Justyn Pisa, Daniela Stangherlin, Kristy-Anne Mackie, Jordan Hochman

Bibliographic record

VenueInternational Journal of Audiology · 2025
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsUniversity of ManitobaUniversity of WinnipegHealth Sciences CentreManitoba Beekeepers' AssociationWinnipeg Regional Health AuthorityManitoba Health
Fundersnot available
KeywordsSpeech perceptionCochlear implantCochlear implantationHearing lossPerceptionDuration (music)

Abstract

fetched live from OpenAlex

Objective Variability in cochlear implant (CI) performance has traditionally been linked to pre-operative factors. This study examined the impact of daily CI wear time on post-operative performance in adult recipients.Design and Study Sample A retrospective analysis was conducted on 158 adult CI users comparing pre- and post-operative factors contributing to performance after one year of use. Participants were divided into post hoc groups for analysis: those wearing their CI less than 10 hours per day (n = 46) and those wearing it 10 or more hours per day (n = 112).Results Daily CI wear time was correlated with speech perception scores (CNC Words, r = 0.44, AzBio, r = 0.37). Regression analyses found that average wear time and age significantly predicted AzBio scores (R2 = 0.21) while wear time and duration of hearing loss predicted CNC Scores (R2 = 0.28).Conclusion Average daily CI wear time, age at implantation and duration of hearing loss are reliable predictors of speech perception scores. Individuals wearing their CI for at least 10 hours per day scored significantly higher than those wearing it less than 10 hours per day. Data suggest earliest possible implantation and CI use of at least 10 hours per day is beneficial.

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 score0.935
Threshold uncertainty score0.285

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 head0.013
GPT teacher head0.316
Teacher spread0.303 · 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 routes1
Has abstractyes

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