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2009· article· en· W2339017763 on OpenAlexfundno aff

Bibliographic record

VenueDevelopmental Medicine & Child Neurology · 2009
Typearticle
Languageen
FieldPsychology
TopicPsychology of Development and Education
Canadian institutionsnot available
FundersScuola Superiore Sant'AnnaUniversità di PisaSick Kids Foundation
KeywordsCitationMedicineLibrary sciencePsychologyComputer science

Abstract

fetched live from OpenAlex

Although Rolandic epilepsy (RE) is considered to be unrelated to severe encephalopathy, there is evidence about the disturbance of certain cognitive functions, especially speech and language. However, opinion regarding the relationship of cognitive decline and age at seizure onset is conflicting. Aim of the study: To assess the relationship of language function and age at seizure onset in children with RE. Methods: We studied language abilities (verbal fluency, speeded naming and comprehension of instructions) by using NEPSY test (A Developmental Neuropsychological Assessment) in 61 patients (pts) with RE aged 5 to 14 years admitted toKaunasUniversityHospital as inpatients.Age at seizure onset was considered the age of the patient at the first unprovoked seizure. Two-step Cluster Analysis and Schwarz’s Bayesian Criterion, ANOVA, Kruskal–Wallis Test andDunn’s post hoc test were used for statistical analysis. Results: Mean age at seizure onset was 7.8±2.4 years, divided into three age clusters (groups): <6 years–13pts (I), 6 to 9 years–29 pts (II), and >9 years–19 pts (III). Mean results of the verbal fluency task were 21.2 (SD13.3) in group I, 37.2 (SD12.1) in group II, 48.7 (SD9.3) in group III (p<0.01). Just the number of errors in speeded naming task did not differ within the groups, while the time of speeded naming task accomplishment and the comprehension of instructions (part I and II) showed correlation with age at onset, early age being determinant of less favorable results (p<0.05). Conclusion: Language difficulties in children with RE showed relationship to early age of seizure onset.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.703
Threshold uncertainty score0.999

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)0.0070.001

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 head0.036
GPT teacher head0.351
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; both teacher heads agree on what is shown here.

Study designNot applicable
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
Published2009
Admission routes1
Has abstractyes

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