Yet More Pediatric Injuries Associated with All-Terrain Vehicles: Should Kids Be Using Them?
Bibliographic record
Abstract
BACKGROUND: Use of all-terrain vehicles (ATVs) has become a popular recreational activity for all ages, yet children suffer a markedly higher proportion of ATV-related injuries and deaths compared with the adult population. Evidence needs to be developed to direct policy to eliminate unnecessary injuries in this population. METHODS: A review of children younger than 16 years old admitted to a tertiary pediatric trauma center with ATV-related injuries over 12.5 years was conducted. Data included demographics, mechanisms of injury, use of helmets, and outcomes. RESULTS: Of 92 patients, 79% were male, the mean age was 12.1 years, 16% were under 10 years old, and 10% had attention deficit hyperactivity disorder (general population prevalence, 1-4%). Flipping/rolling was the most common mechanism of injury (32%) and was associated with sustaining chest and abdominal injuries (odds ratio, 3.7; 95% confidence interval, 1.1-12.9) and an increased Injury Severity Score (ISS). Only 4 of 20 patients with head/facial injuries were using helmets compared with 37 of 40 (odds ratio of head injury with helmet use, 0.02; 95% confidence interval, 0.004-0.101). Forty-five patients had more than one body system injured. The mean ISS was 7.0 (range, 1-35), and high scores were associated with sustaining head and/or truncal injuries. Fourteen percent of cases had an ISS greater than or equal to 12 and two patients died. The median length of stay was 3 days; 12% were admitted over 2 weeks. CONCLUSION: Children continue to sustain a large proportion of preventable and unnecessary injuries caused by ATVs. Although use of protective devices (i.e., helmets) diminishes the extent of injuries, children's smaller size relative to these large machines may contribute to flipping, rolling, and loss of control. Increased risk-taking by those with attention deficit hyperactivity disorder may also play a role. Efforts are needed to lobby for policies to limit the promotion and restrict the use of ATVs by children.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".