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Record W2273907215 · doi:10.1177/0003489415619943

Emotional Perception of Music in Children With Bimodal Fitting and Unilateral Cochlear Implant

2015· article· en· W2273907215 on OpenAlexaff
Sareh Shirvani, Zahra Jafari, Masoud Motasaddi Zarandy, Shohre Jalaie, Hamed Mohagheghi, Mohammad Reza Tale

Bibliographic record

VenueAnnals of Otology Rhinology & Laryngology · 2015
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsUniversity of Lethbridge
FundersTehran University of Medical Sciences and Health Services
KeywordsCochlear implantAudiologyFeelingPerceptionMusic perceptionPsychologyHearing lossDevelopmental psychologyMedicineSocial psychology

Abstract

fetched live from OpenAlex

OBJECTIVE: Biological, structural, and acoustical constraints faced by cochlear implant (CI) users can alter the perception of music. Bimodal fitting not only provides bilateral hearing but can also improve auditory skills. This study was conducted to assess the impact of this amplification style on the emotional perception of music among children with hearing loss (HL). METHODS: Twenty-five children with congenital severe to profound HL and unilateral CIs, 20 children with bimodal fitting, and 30 children with normal hearing participated in this study. Their emotional perceptions of music were measured using a method where children indicated happy or sad feelings induced by music by pointing to pictures of faces showing these emotions. RESULTS: Children with bimodal fitting obtained significantly higher mean scores than children with unilateral CIs for both happy and sad music items and in overall test scores (P < .001). Both groups with HL obtained significantly lower scores than children with normal hearing (P < .001). CONCLUSIONS: Bimodal fitting results in a better emotional perception of music compared to unilateral CI. Given the influence of music in neurological and linguistic development and social interactions, it is important to evaluate the possible benefits of bimodal fitting prescriptions for individuals with unilateral CIs.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.078
Threshold uncertainty score0.434

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 science0.0000.000
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 head0.064
GPT teacher head0.306
Teacher spread0.242 · 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

Citations31
Published2015
Admission routes1
Has abstractyes

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