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Record W48271387

Language impairments in children with fetal alcohol spectrum disorders.

2011· article· en· W48271387 on OpenAlexaff
Katherine Wyper, Carmen Rasmussen

Bibliographic record

VenuePubMed · 2011
Typearticle
Languageen
FieldMedicine
TopicPrenatal Substance Exposure Effects
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPsychologyImitationFetal alcoholDevelopmental psychologyComprehensionCognitionPrenatal alcohol exposurePsychological interventionSentenceLanguage developmentFetal alcohol syndromeLanguage delayVocabularyPopulationMedicinePsychiatryPregnancyLinguistics
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND: Fetal Alcohol Spectrum Disorder (FASD) is associated with a range of disabilities, including physical, behavioural, and cognitive deficits. One specific area of concern in children with FASD is the use and development of speech and language. Language deficits in FASD have been linked to learning problems and social difficulties. OBJECTIVES: The current study sought to examine the language difficulties of children with FASD, and to identify areas of deficit that may be particularly pronounced among these children. METHODS: Fifty children, aged 5 to 13, (27 with FASD, 23 control children) were tested on the CREVT-2, the TOLD-P:3, and the TOLD-I:3. RESULTS: Children with FASD had significantly lower scores than control children on both receptive and expressive subtests of the CREVT-2. Younger children scored significantly lower than controls on the Relational Vocabulary and Sentence Imitation subtests of the TOLD-P:3, and older children were significantly delayed on the Word Ordering, Grammatic Comprehension, and Malapropisms subtests of the TOLD-I:3. CONCLUSIONS: This study identified several areas of marked difficulty in children with FASD, adding to the current understanding of language development in this population. The results have implications for tailoring early interventions, and for providing evidence-based support to children prenatally exposed to alcohol.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.455

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 head0.009
GPT teacher head0.207
Teacher spread0.198 · 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 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

Citations40
Published2011
Admission routes1
Has abstractyes

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