MétaCan
Menu
Back to cohort
Record W4393004475 · doi:10.3390/brainsci14030291

Auditory Processing and Speech-Sound Disorders

2024· article· en· W4393004475 on OpenAlexaboutno aff
Konstantinos Drosos, Alexandra Papanicolaou, Louiza Voniati, Klea Panayidou, Chryssoula Thodi

Bibliographic record

VenueBrain Sciences · 2024
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsnot available
Fundersnot available
KeywordsRecallPhonological awarenessMEDLINEPsychologyCognitionAudiologyScopusPsychophysiologyLiteracyMedicineCognitive psychologyPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Speech-sound disorders (SSD) have been linked to auditory processing difficulties, and auditory processing disorders (APD) have been related to phonological awareness and literacy development. To this date, there has not been a systematic literature review investigating the results of psychophysiology and language assessments related to SSD and APD in children. METHODS: The literature search was conducted in PubMed, Medline EBSCO, and Scopus to identify studies with children diagnosed/suspected of having APDs and SSDs. The quality of methodology in the selected articles was evaluated with the Newcastle Ottawa Scale. RESULTS: Seven out of 378 relevant studies met the selection criteria. The findings were summarized for children with SSD and APD based on (a) metalinguistic and literacy skills, (b) cognitive abilities, and (c) temporal processing abilities. Three articles indicated that children with APD and SSD exhibit lower temporal task accuracy and reaction time. In two studies, children with SSD exhibited lower scores in discrimination, sequencing, and recall of brief stimuli in rapid succession. CONCLUSIONS: This review revealed associations between SSD severity and APD that may underline low performance in metalinguistic skills. Diagnostic assessments have been proposed based on the review to adequately identify children with SSD and APD and provide useful information for more suitable intervention.

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 score0.751
Threshold uncertainty score0.659

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
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 head0.049
GPT teacher head0.331
Teacher spread0.281 · 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

Citations10
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueBrain SciencesSame topicNeuroscience and Music PerceptionFrench-language works237,207