Incidence of Subsequent Mental Health Disorders and Social Adversity Following Pediatric Concussion: A Longitudinal, Population-Based Study
Bibliographic record
Abstract
OBJECTIVE: To determine the long-term risk of new adverse psychosocial outcomes among adolescents diagnosed with a concussion compared with those not diagnosed. STUDY DESIGN: A retrospective, population-based cohort study was conducted. Adolescents (10-18 years) with a physician-diagnosed concussion between 2000 and 2005 were matched on neighborhood and age with 5 controls without concussion from the general population. New-onset mental health disorders, medication use, social, and justice outcomes were extracted using datasets linked to the population data repository. Adolescents were followed for 11-16 years. Adjusted hazard ratios (95% CIs) were estimated. RESULTS: In total, 2082 adolescents with a concussion were matched to 10 510 without. Adolescents with a concussion had an increased risk of any mental health disorder (HR 1.34; 95% CI 1.25-1.45), mood disorder (HR 1.30; 95% 1.18-1.43), psychosis (HR 1.43; 95% CI 1.18-1.74), substance abuse disorder (HR 1.67; 95% 1.31-2.14), and receiving a psychotropic prescription (HR 1.31; 95% CI 1.20-1.42). Female adolescents had an increased risk of ADHD following concussion (HR 1.89; 95% CI 1.17-3.05). Adolescents with a concussion had an increased risk of being accused (HR 1.22; 95% CI 1.11-1.34), victim (HR 1.29; 95% CI 1.11-1.48), or witness (HR 1.16; 95% CI 1.01-1.32) of a crime, or contact with Child and Family Services (HR 1.33; 95% CI 1.10-1.62). There was no association between concussion and attempting or completing suicide, receiving housing support, or collecting income support. CONCLUSIONS: Concussion was associated with an increased risk for multiple adverse psychosocial outcomes. Future work should focus on early identification of those at risk of these outcomes to help optimize longitudinal medical care and support.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".