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Record W2111077259 · doi:10.1093/arclin/acu004

Frontal White Matter Damage Impairs Response Inhibition in Children Following Traumatic Brain Injury

2014· article· en· W2111077259 on OpenAlexafffund
Jonathan Lipszyc, Harvey S. Levin, Gerri Hanten, Jill V. Hunter, Maureen Dennis, Russell Schachar

Bibliographic record

VenueArchives of Clinical Neuropsychology · 2014
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsUniversity of TorontoHospital for Sick Children
FundersNational Institutes of HealthCanadian Institutes of Health ResearchHospital for Sick ChildrenNational Institute of Neurological Disorders and StrokeUniversity of Toronto
KeywordsTraumatic brain injuryWhite matterFrontal lobePsychologyNeuroscienceInferior frontal gyrusSuperior frontal gyrusResponse inhibitionBrain damageConcussionCognitionMagnetic resonance imagingAudiologyMedicinePoison controlInjury preventionPsychiatry

Abstract

fetched live from OpenAlex

Inhibition, the ability to suppress inappropriate cognitions or behaviors, can be measured using computer tasks and questionnaires. Inhibition depends on the frontal cortex, but the role of the underlying white matter (WM) is unclear. We assessed the specific impact of frontal WM damage on inhibition in 29 children with moderate-to-severe traumatic brain injury (15 with and 14 without frontal WM damage), 21 children with orthopedic injury, and 29 population controls. We used the Stop Signal Task to measure response inhibition, the Behavior Rating Inventory of Executive Function to assess everyday inhibition, and T2 fluid-attenuated inversion recovery magnetic resonance imaging to identify lesions. Children with frontal WM damage had impaired response inhibition compared with all other groups and poorer everyday inhibition than the orthopedic injury group. Frontal WM lesions most often affected the superior frontal gyrus. These results provide evidence for the critical role of frontal WM in inhibition.

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)0.003
metaresearch head score (Gemma)0.004
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 score0.021
Threshold uncertainty score0.870

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.054
GPT teacher head0.417
Teacher spread0.364 · 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

Citations21
Published2014
Admission routes2
Has abstractyes

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