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Record W2008952307 · doi:10.1037/a0032837

Heterogeneity of brain lesions in pediatric traumatic brain injury.

2013· article· en· W2008952307 on OpenAlexaff
Erin D. Bigler, Tracy J. Abildskov, JoAnn Petrie, Thomas J. Farrer, Maureen Dennis, Nevena Simić, H. Gerry Taylor, Kenneth H. Rubin, Kathryn Vannatta, Cynthia A. Gerhardt, Terry Stancin, Keith Owen Yeates

Bibliographic record

VenueNeuropsychology · 2013
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsHospital for Sick Children
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute of Child Health and Human Development
KeywordsCorpus callosumTraumatic brain injuryHyperintensityNeuroimagingAtrophyWhite matterMagnetic resonance imagingMedicinePathologyDiffuse axonal injuryEncephalomalaciaPsychologySpleniumBasal gangliaNeuroscienceRadiologyPsychiatryCentral nervous system

Abstract

fetched live from OpenAlex

OBJECTIVE: Magnetic resonance imaging (MRI) provides a method to identify and quantify abnormalities resulting from traumatic brain injury (TBI). MRI abnormalities in children with TBI have not been fully characterized according to the frequency, location, and quantitative measurement of a range of pathologies critical for studies of neuropsychological outcome. Here, we report MRI findings from a large, multicenter study of childhood TBI, the Social Outcomes of Brain Injury in Kids (SOBIK) study, which compared qualitative and quantitative neuroimaging findings in 72 children with complicated mild-to-severe TBI to 52 children with orthopedic injury (OI). METHOD: Qualitative analyses of MRI scans coded white matter hyperintensities (WMHs), hemosiderin deposits reflecting prior hemorrhagic lesions, regions of encephalomalacia and/or atrophy, and corpus callosum atrophy and traumatic shear lesions. Two automated quantitative analyses were conducted: (a) FreeSurfer methods computed volumes for total brain, white matter (WM), gray matter (GM), corpus callosum, ventricles, amygdala, hippocampus, basal ganglia, and thalamus along with a ventricle-to-brain ratio (VBR); and (b) voxel-based morphometry (VBM) to identify WM, GM, and cerebrospinal fluid. We also examined performance on the Processing Speed Index (PSI) from the Wechsler Intelligence Scale for Children, Fourth Edition, in relation to the above-mentioned neuroimaging variables. RESULTS: WMHs, hemosiderin deposits, and focal areas of encephalomalacia or atrophy were common in children with TBI, were related to injury severity, and were mostly observed within a frontotemporal distribution. Quantitative analyses showed volumetric changes related to injury severity, especially ventricular enlargement and reduced corpus callosum volume. VBM demonstrated similar findings, but, in addition, GM reductions in the inferior frontal, basal forebrain region, especially in the severe TBI group. The complicated mild TBI group showed few differences from the OI group. PSI was significantly associated with global atrophy, as measured by VBR. CONCLUSION: MRI findings after childhood TBI are diverse and particularly influenced by injury severity, and they involve common features, group heterogeneity, and individual variability.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.099
GPT teacher head0.400
Teacher spread0.301 · 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 source (direct Gemma or distilled Codex), 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

Citations113
Published2013
Admission routes1
Has abstractyes

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