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Record W2158960857 · doi:10.3389/fpsyg.2013.00719

Benefits and detriments of unilateral cochlear implant use on bilateral auditory development in children who are deaf

2013· review· en· W2158960857 on OpenAlexafffund
Karen A. Gordon, Salima Jiwani, Blake C. Papsin

Bibliographic record

VenueFrontiers in Psychology · 2013
Typereview
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsHospital for Sick ChildrenSickKids FoundationUniversity of Toronto
FundersHospital for Sick Children
KeywordsCochlear implantAudiologyPsychologyImplantHearing lossCochlear implantationLanguage developmentBrainstemSpeech perceptionMedicineNeurosciencePerceptionDevelopmental psychologySurgery

Abstract

fetched live from OpenAlex

We have explored both the benefits and detriments of providing electrical input through a cochlear implant in one ear to the auditory system of young children. A cochlear implant delivers electrical pulses to stimulate the auditory nerve, providing children who are deaf with access to sound. The goals of implantation are to restrict reorganization of the deprived immature auditory brain and promote development of hearing and spoken language. It is clear that limiting the duration of deprivation is a key factor. Additional considerations are the onset, etiology, and use of residual hearing as each of these can have unique effects on auditory development in the pre-implant period. New findings show that many children receiving unilateral cochlear implants are developing mature-like brainstem and thalamo-cortical responses to sound with long term use despite these sources of variability; however, there remain considerable abnormalities in cortical function. The most apparent, determined by implanting the other ear and measuring responses to acute stimulation, is a loss of normal cortical response from the deprived ear. Recent data reveal that this can be avoided in children by early implantation of both ears simultaneously or with limited delay. We conclude that auditory development requires input early in development and from both ears.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.944
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
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 head0.057
GPT teacher head0.334
Teacher spread0.277 · 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.

Study designOther design
Domainnot available
GenreReview

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

Citations80
Published2013
Admission routes2
Has abstractyes

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