Psychiatric Disorders in Children and Adolescents 24 Months After Mild Traumatic Brain Injury
Bibliographic record
Abstract
This study aimed to better understand the occurrence of novel psychiatric disorders (NPDs) in children with mild traumatic brain injury (mTBI) in relation to preinjury variables, injury-related variables, and concurrent neurocognitive outcome. Eighty-seven children aged 5-14 years who had experienced mTBI were studied from consecutive hospital admissions with semistructured psychiatric interviews soon after injury (baseline). Fifty-four children were reassessed 24 months postinjury. Standardized instruments were used to evaluate injury severity, lesion characteristics, preinjury variables (lifetime psychiatric disorder, family psychiatric history, family function, socioeconomic status, psychosocial adversity, adaptive function, and academic function), and finally, postinjury neurocognitive and adaptive function. At 24 months postinjury, NPDs had occurred in 17 of 54 (31%) participants. NPD at 24 months was related to frontal white matter lesions and was associated with estimated preinjury reading, preinjury adaptive function, and concurrent deficits in reading, processing speed, and adaptive function. These findings extend earlier reports that the psychiatric morbidity after mTBI in children is more common than previously thought, and moreover, it is linked to preinjury individual variables and injury characteristics and is associated with postinjury adaptive and neurocognitive functioning.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".