Pediatric violence-related injuries presenting to the emergency department: epidemiology and risk factors
Bibliographic record
Abstract
Background Physical violence frequently brings victims to seek care in emergency departments, providing opportunity for prevention. Objective To describe physical aggression-related injuries in youth 5–19 years presenting to an urban emergency department and identify risk factors. Methods We used retrospective data from one paediatric tertiary centre affiliated with the Canadian Hospitals Injury Reporting and Prevention Program. Intentional injuries inflicted by other youth were examined from 1998 to 2007. Injuries were classified by age groups and characterised according to type, body part involved, place where they occurred, mechanism and disposition of the patient. Results Five hundred four visits for non-accidental physical injuries were identified. There were 395 (78%) males. Thirty-nine (8%) patients were 5–9 years-old, 251 (50%) were 10–14 years-old and 214 (42%) were 15–19 years-old. Injuries were superficial (23%), open wounds (21%), concussions (19%) and fractures (17%). The head and face were involved most frequently (63%). When information was available, 50% of injuries occurred at school (153 of 306), and 63% involved bodily force (190 of 300). Twenty-five (5%) were admitted to the hospital for more severe injuries. Conclusion In this study, most physical aggression-related injuries in youth occurred in 10–19 year-old boys, involved the head and face, occurred at school and resulted from bodily force. A small minority of patients were admitted. Youth that have been assaulted can be targeted for intervention and follow-up in order to manage the physical and psychological sequelae, as well as prevent their repeated victimisation, their possible evolution into perpetrators and enhance their empowerment.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".