Road Trauma in Teenage Male Youth with Childhood Disruptive Behavior Disorders: A Population Based Analysis
Bibliographic record
Abstract
BACKGROUND: Teenage male drivers contribute to a large number of serious road crashes despite low rates of driving and excellent physical health. We examined the amount of road trauma involving teenage male youth that might be explained by prior disruptive behavior disorders (attention deficit hyperactivity disorder, conduct disorder, oppositional defiant disorder). METHODS AND FINDINGS: We conducted a population-based case-control study of consecutive male youth between age 16 and 19 years hospitalized for road trauma (cases) or appendicitis (controls) in Ontario, Canada over 7 years (April 1, 2002 through March 31, 2009). Using universal health care databases, we identified prior psychiatric diagnoses for each individual during the decade before admission. Overall, a total of 3,421 patients were admitted for road trauma (cases) and 3,812 for appendicitis (controls). A history of disruptive behavior disorders was significantly more frequent among trauma patients than controls (767 of 3,421 versus 664 of 3,812), equal to a one-third increase in the relative risk of road trauma (odds ratio = 1.37, 95% confidence interval 1.22-1.54, p<0.001). The risk was evident over a range of settings and after adjustment for measured confounders (odds ratio 1.38, 95% confidence interval 1.21-1.56, p<0.001). The risk explained about one-in-20 crashes, was apparent years before the event, extended to those who died, and persisted among those involved as pedestrians. CONCLUSIONS: Disruptive behavior disorders explain a significant amount of road trauma in teenage male youth. Programs addressing such disorders should be considered to prevent injuries.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".