Immature Auditory Evoked Potentials in Children With Moderate–Severe Developmental Language Disorder
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".