Association between pediatric TBI and mental health and substance use disorders: A scoping review
Bibliographic record
Abstract
BACKGROUND: The relationship between pediatric Traumatic Brain Injury (TBI) and long-term mental health and substance use disorders is not well known, resulting in inadequate prevention and management strategies. The aim of this scoping review is to review the evidence on pediatric TBI and the development of mental health disorders and substance use later in life and to identify gaps in the literature to inform future research. METHODS: We searched multiple databases for original articles published between September 2002 and September 2022 on TBI-related mental health and/or substance use disorders in children and youth. Two independent reviewers performed the screening using Arksey and O'Malley and Levac et al.'s scoping review framework. RESULTS: A total of six papers are included in this scoping review. Studies included are comprised of cross-sectional and prospective longitudinal cohort studies. DISCUSSION: A correlation between pediatric TBI and development of certain mental health disorders and substance use is suggested, although much of the current evidence is mixed and does not account for confounding variables. Future studies should aim to closely examine these links and identify modifiers that can influence these relationships.
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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 | 0.008 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.017 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".