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Record W2871654857 · doi:10.1044/2018_jslhr-l-17-0420

Immature Auditory Evoked Potentials in Children With Moderate–Severe Developmental Language Disorder

2018· article· en· W2871654857 on OpenAlexaff
Elaine Yuen Ling Kwok, Marc F. Joanisse, Lisa M. D. Archibald, Janis Oram Cardy

Bibliographic record

VenueJournal of Speech Language and Hearing Research · 2018
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsWestern University
Fundersnot available
KeywordsAudiologyPsychologyNormativeIntraclass correlationLanguage developmentDevelopmental psychologyEl NiñoMedicinePsychometricsPediatrics

Abstract

fetched live from OpenAlex

Purpose: Immature auditory processing has been proposed to underlie language impairments in children with developmental language disorder (DLD; also known as specific language impairment). Using newly available normative auditory evoked potential (AEP) waveforms, we estimated AEP maturity in individual children with DLD and explored whether this maturational index was related to their language abilities. Method: AEPs were elicited by 225 trials of a 490-Hz pure tone. Using intraclass correlation and our previously established normative AEP waveforms of 7- to 10-year-old children with typical development, we estimated the age equivalent of the AEPs (AEP-age) from 21 children with DLD. The relation between AEP maturity and language was explored through regression analysis. Results: AEP-age predicted 31% of the variance in the language abilities of children with DLD. The AEP-age of children with mild DLD was similar to their chronological age, whereas children with moderate-severe DLD showed, on average, a 1.3-year delay in their neural responses. AEP-age predicted receptive, but not expressive, language performance. Conclusion: Maturation in auditory neural responses is a significant predictor of language ability, particularly in children with moderate-severe DLD.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.714
Threshold uncertainty score0.313

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.038
GPT teacher head0.345
Teacher spread0.307 · 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 designBench or experimental
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

Citations21
Published2018
Admission routes1
Has abstractyes

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