ATVs: motorized toys or vehicles for children?
Bibliographic record
Abstract
OBJECTIVES: To compare the nature of injuries from all-terrain vehicles (ATVs) to those from bicycling, dirtbikes/motocross, and motor vehicle crashes. DESIGN: Data on injuries from the mechanisms outlined above were obtained through CHIRPP (the Canadian Hospitals Injury Reporting and Prevention Program) and hospital records. SETTING: A Canadian tertiary pediatric center. SUBJECTS: Cases presenting to the emergency department over a 10 year period. MAIN OUTCOME MEASURES: Comparison between demographics, mechanisms and natures of injuries sustained, disposition from the emergency department, and lengths of hospital stay. RESULTS: Contrary to bicycling, ATV related injuries occurred among older ages and appeared to result less often from loss of control. Severe injuries resulting in deep soft tissue trauma and fracture/dislocations were 1.7 and 1.5 times, respectively, more frequent among ATV trauma than bicycling (p<0.01). In addition, ATV related injuries were located more frequently in the trunkal, hip, lower extremity, and spinal regions. Conversely, ATV related trauma bore significant similarities regarding body part and nature of the injury to both motor vehicle crash (MVC) and dirtbike related injuries. Akin to dirtbike and MVC related trauma, ATV related injuries more frequently required admission to the ward or intensive care unit compared to bicycling injuries (30.8% v 9.6%, p<0.0001), and used a proportionally larger amount of hospital resources with respect to overall in-hospital and intensive care unit days. CONCLUSIONS: Although ATVs may be considered recreational for children, their associated injury patterns, severity, and costs to the healthcare system more closely resemble those from motorized vehicles and are more significant than bicycling. Strict policy to reflect this must be developed and acknowledged by the public, industry, and legislative bodies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".