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Record W2008536692 · doi:10.1097/aud.0b013e3182a4a8f0

Longitudinal Speech Perception and Language Performance in Pediatric Cochlear Implant Users

2013· article· en· W2008536692 on OpenAlexaff
Camille C. Dunn, Elizabeth Walker, Jacob Oleson, Maura Kenworthy, Tanya Van Voorst, J. Bruce Tomblin, Haihong Ji, Karen Iler Kirk, Bob McMurray, Marlan Hanson, Bruce J. Gantz

Bibliographic record

VenueEar and Hearing · 2013
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsCentre for Movement Disorders
FundersNational Center for Research ResourcesNational Institute on Deafness and Other Communication Disorders
KeywordsAudiologyCochlear implantSpeech perceptionCochlear implantationPerceptionPsychologyHearing lossLanguage developmentMedicineReading (process)Age groupsLongitudinal studyDevelopmental psychologyDemographyLinguistics

Abstract

fetched live from OpenAlex

In Brief Objectives: Few studies have examined the long-term effect of age at implantation on outcomes using multiple data points in children with cochlear implants. The goal of this study was to determine whether age at implantation has a significant, lasting impact on speech perception, language, and reading performance for children with prelingual hearing loss. Design: A linear mixed-model framework was used to determine the effect of age at implantation on speech perception, language, and reading abilities in 83 children with prelingual hearing loss who received cochlear implants by the age of 4 years. The children were divided into two groups based on their age at implantation: (1) under 2 years of age and (2) between 2 and 3.9 years of age. Differences in model-specified mean scores between groups were compared at annual intervals from 5 to 13 years of age for speech perception, and 7 to 11 years of age for language and reading. Results: After controlling for communication mode, device configuration, and preoperative pure-tone average, there was no significant effect of age at implantation for receptive language by 8 years of age, expressive language by 10 years of age, reading by 7 years of age. In terms of speech-perception outcomes, significance varied between 7 and 13 years of age, with no significant difference in speech-perception scores between groups at ages 7, 11, and 13 years. Children who used oral communication (OC) demonstrated significantly higher speech-perception scores than children who used total communication (TC). OC users tended to have higher expressive language scores than TC users, although this did not reach significance. There was no significant difference between OC and TC users for receptive language or reading scores. Conclusions: Speech perception, language, and reading performance continue to improve over time for children implanted before 4 years of age. The present results indicate that the effect of age at implantation diminishes with time, particularly for higher-order skills such as language and reading. Some children who receive cochlear implants after the age of 2 years have the capacity to approximate the language and reading skills of their earlier-implanted peers, suggesting that additional factors may moderate the influence of age at implantation on outcomes over time. Few studies have examined longitudinal trends and the effect(s) of age at implantation by using multiple data points in children with cochlear implants. Speech perception, language, and reading were analyzed in 83 children divided into two groups: (1) implanted under 2 years of age and (2) implanted between 2 and 3.9 years of age. Performance indicated that although implanting children at a younger age is important, children who are implanted between 2 to 3.9 years of age have the potential to close the gap with their younger implanted peers on some outcomes.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.027
GPT teacher head0.273
Teacher spread0.246 · 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 source (direct Gemma or distilled Codex), 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

Citations172
Published2013
Admission routes1
Has abstractyes

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