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Record W2562840986 · doi:10.1089/neu.2016.4594

Social and Behavioral Outcomes following Childhood Traumatic Brain Injury: What Predicts Outcome at 12 Months Post-Insult?

2016· article· en· W2562840986 on OpenAlexaff
Cathy Catroppa, Stephen Hearps, Louise Crossley, Keith Owen Yeates, Miriam H. Beauchamp, Jessica Fusella, Vicki Anderson

Bibliographic record

VenueJournal of Neurotrauma · 2016
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsTraumatic brain injuryInjury preventionMedicineClinical psychologyPsychologyPoison controlPsychiatryPediatricsMedical emergency

Abstract

fetched live from OpenAlex

This study sought to investigate social and behavioral outcomes 12 months following childhood traumatic brain injury (TBI) and to identify predictors of these outcomes. The study also compared rates of impairment in social and behavioral outcomes at 12 months post-injury between children with TBI and a typically developing (TD) control group. The study comprised 114 children ages 5.5 to 16.0 years, 79 with mild, moderate, or severe TBI and 35 TD children, group-matched for age, sex and socio-economic status. Children with TBI were recruited via consecutive hospital admissions and TD children from the community. Social and behavioral outcomes were measured via parent-rated questionnaires. Analysis of covariance models identified a significant mean difference between the mild and moderate groups for social problems only, but the moderate and severe TBI groups showed a higher rate of impairment, particularly in externalizing problems. Pre-injury function, injury severity, parent mental health, and child self-esteem all contributed significantly to predicting social and behavioral outcomes. Both injury and non-injury factors should be considered when identifying children at risk for long-term difficulties in social and behavioral domains.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.256
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.172
GPT teacher head0.422
Teacher spread0.249 · 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.

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

Citations42
Published2016
Admission routes1
Has abstractyes

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