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Record W4386244138 · doi:10.1111/mbe.12379

Lost in Translation: A Preliminary Examination of Phonological Processing, Language, and Reading in Childhood Epilepsy

2023· article· en· W4386244138 on OpenAlexafffund
Katharine Bailey, Nancie Im‐Bolter

Bibliographic record

VenueMind Brain and Education · 2023
Typearticle
Languageen
FieldMedicine
TopicEpilepsy research and treatment
Canadian institutionsQueen's UniversityTrent University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsReading (process)EpilepsyPsychologyCognitionPhonological awarenessReading comprehensionPhonologyPopulationCognitive psychologyDevelopmental psychologyLinguisticsMedicinePsychiatry

Abstract

fetched live from OpenAlex

ABSTRACT Children with epilepsy are at increased risk for language and reading difficulties. Phonological processing and language underlie reading success, yet their association with reading achievement in children with epilepsy is unknown. We assessed phonological processing, oral language, and reading in children (ages 6 to 12) with epilepsy (EP; n = 6), language problems (LP; n = 14), and typical development (TD; n = 15). The EP and LP group had lower scores on all measures compared with the TD group, with the EP group scoring the lowest on reading comprehension. Phonological processing followed by epilepsy diagnosis predicted reading decoding. Language followed by epilepsy diagnosis predicted reading comprehension. Thus, reading problems in children with epilepsy are not fully explained by their phonological processing or language deficits. Further research that considers other cognitive processes important for reading (e.g., executive function) in this population may help inform our understanding of their reading success.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.829
Threshold uncertainty score0.186

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 head0.025
GPT teacher head0.329
Teacher spread0.304 · 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

Citations1
Published2023
Admission routes2
Has abstractyes

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