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Record W4405157184 · doi:10.1002/dev.70013

Predictive Strength of Auditory Maturity Across Different Levels of Language Ability: An Exploratory Quantile Regression Study

2024· article· en· W4405157184 on OpenAlexafffund
Theresa Pham, Alyssa Janes, Elaine Yuen Ling Kwok, Janis Oram Cardy

Bibliographic record

VenueDevelopmental Psychobiology · 2024
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of CanadaScottish Rite Charitable Foundation of CanadaOrganization for Autism Research
KeywordsQuantile regressionPsychologyLinear regressionRegressionRegression analysisAudiologyQuantileDevelopmental psychologyStatisticsMathematicsMedicine

Abstract

fetched live from OpenAlex

Auditory evoked potential-age (AEP-age) is proposed to index auditory maturation and has been found to predict language skills in children with and without a language disorder. However, reporting average effects using linear regression does not fully capitalize on the potential of AEP-age to estimate individual differences in young children. This study used a quantile regression approach to examine the predictive utility of AEP-age for 105 typical and neurodiverse 7-10-year-old children (61 males; 44 females; largely monolingual English) with varying language skills without creating subgroups. Although linear regression did not find an association between AEP-age and language skills, the quantile model added specificity by revealing differential associations. AEP-age was only related to language skills for children at around the median point of the language continuum, but, not for those at the lowest or highest end of the language distribution. Overall, the quantile regression methodology provides us with the flexibility of understanding how AEP-age is related to different language abilities.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.784
Threshold uncertainty score0.489

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 studies0.0000.000
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 head0.051
GPT teacher head0.366
Teacher spread0.315 · 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

Citations0
Published2024
Admission routes2
Has abstractyes

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