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Record W2625557934 · doi:10.1121/1.4985123

Music perception improves in children with bilateral cochlear implants or bimodal devices

2017· article· en· W2625557934 on OpenAlexafffundabout
Melissa J. Polonenko, Sara Giannantonio, Blake C. Papsin, Pasquale Marsella, Karen A. Gordon

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2017
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsSickKids FoundationHospital for Sick Children
FundersCanadian Institutes of Health Research
KeywordsCochlear implantAudiologyImplantPerceptionSpeech perceptionMusic perceptionPsychologyCochlear implantationMedicineSurgery

Abstract

fetched live from OpenAlex

The objectives of this study were to determine if music perception by pediatric cochlear implant users can be improved by (1) providing access to bilateral hearing through two cochlear implants or a cochlear implant and a contralateral hearing aid (bimodal users) and (2) any history of music training. The Montreal Battery of Evaluation of Musical Ability test was presented via soundfield to 26 bilateral cochlear implant users, 8 bimodal users and 16 children with normal hearing. Response accuracy and reaction time were recorded via an iPad application. Bilateral cochlear implant and bimodal users perceived musical characteristics less accurately and more slowly than children with normal hearing. Children who had music training were faster and more accurate, regardless of their hearing status. Reaction time on specific subtests decreased with age, years of musical training and, for implant users, better residual hearing. Despite effects of these factors on reaction time, bimodal and bilateral cochlear implant users' responses were less accurate than those of their normal hearing peers. This means children using bilateral cochlear implants and bimodal devices continue to experience challenges perceiving music that are related to hearing impairment and/or device limitations during development.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.957
Threshold uncertainty score0.331

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.023
GPT teacher head0.285
Teacher spread0.261 · 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

Citations39
Published2017
Admission routes3
Has abstractyes

Explore more

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