MétaCan
Menu
Back to cohort
Record W1991137351 · doi:10.1177/0883073808315620

Neurological and Magnetic Resonance Imaging Findings in Children With Developmental Language Impairment

2008· article· en· W1991137351 on OpenAlexaff
Richard Webster, Caroline Erdos, Karen Evans, Annette Majnemer, Gaurav Saigal, Eva Kehayia, Elin Thordardottir, Alan C. Evans, Michael Shevell

Bibliographic record

VenueJournal of Child Neurology · 2008
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsMcGill Genome CentreMcGill University
Fundersnot available
KeywordsMagnetic resonance imagingSpecific language impairmentLanguage impairmentPsychologyNonverbal communicationNeurological examinationModalitiesAudiologyPopulationMedicineDevelopmental psychologyNeuroscienceRadiology

Abstract

fetched live from OpenAlex

Neurologic and radiologic findings in children with well-defined developmental language impairment have rarely been systematically assessed. Children aged 7 to 13 years with developmental language impairment or normal language (controls) underwent language, nonverbal cognitive, motor and neurological assessments, standardized assessment for subtle neurological signs, and magnetic resonance imaging. Nine children with developmental language impairment and 12 controls participated. No focal abnormalities were identified on standard neurological examination. Age and developmental language impairment were independent predictors of neurological subtle signs scores (r(2) = 0.52). Imaging abnormalities were identified in two boys with developmental language impairment and no controls (P = .17). Lesions identified were predicted neither by history nor by neurological examination. Previously unsuspected lesions were identified in almost 25% of children with developmental language impairment. Constraints regarding cooperation and sedation requirements may limit the clinical application of imaging modalities in this population.

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 score0.040
Threshold uncertainty score0.507

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 head0.005
GPT teacher head0.219
Teacher spread0.214 · 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

Citations12
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Child NeurologySame topicLanguage Development and DisordersFrench-language works237,207