Predictors of Long-Term Victimization After Early Pediatric Traumatic Brain Injury
Bibliographic record
Abstract
OBJECTIVE: Pediatric traumatic brain injuries (TBIs) adversely affect long-term functional and social outcomes. Limited research suggests children with TBI are more likely to be victimized by peers than noninjured children. Deficits in social information processing (SIP), cognitive ability, and executive functioning (EF) may contribute to increased victimization risk. This study examined rates of peer victimization/bullying in children with early TBI compared with children with orthopedic injuries (OIs) and the role of processing speed, executive function (EF), and SIP as mediators of the association of TBI and peer victimization. METHOD: Children ages 10 to 14 years who sustained a complicated mild/moderate or severe TBI (N = 58) or OI (N = 72) during early childhood (ages 3-7 yr) and their parents participated in a longitudinal prospective follow-up 6.8 years postinjury. SIP, EF and processing speed, and peer victimization were assessed. RESULTS: Parents of children with severe TBI reported greater rates of peer victimization than parents of children with OIs. Children with severe TBI demonstrated greater EF deficits than children with complicated mild/moderate TBI or OI and poorer processing speed than children with OI. No significant indirect relationships were found between groups and any outcome variables to indicate mediation. CONCLUSION: Based on parent report, children with severe TBI have higher risk of peer victimization than those with less severe injuries. In addition, children with severe TBI have more impaired EF and cognitive ability than counterparts with less severe TBI. Further research is needed to explore predictors of long-term victimization after early TBI to create interventions aimed at providing social, emotional, and behavioral skill building for victimized youth.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".