Ten Years of All-Terrain Vehicle Injury, Mortality, and Healthcare Costs
Bibliographic record
Abstract
BACKGROUND: All-terrain vehicles (ATVs) are increasing in popularity worldwide. The province of Alberta accounts for 25% of Canadian ATV sales. This study describes the epidemiology, outcomes, and associated healthcare costs for a decade of ATV traumatic injury incidents. METHODS: This is a retrospective population based cohort study using two provincial databases: the Alberta Trauma Registry and the Office of the Chief Medical Examiner of Alberta. Data for individuals aged 18 years or older with Injury Severity Score ≥ 12 or deaths between April 1, 1998, and March 31, 2008 were included. Healthcare costs were extrapolated using figures from a Level I trauma center. RESULTS: ATV incidents resulted in 459 serious trauma cases, 395 trauma center admissions (a total of 4,117 days), and a 17% mortality rate. Postdischarge care was required for nearly 30% of patients. Male patients aged 18 years to 19 years had the highest incidence (6.5 of 100,000 people). Head, neck, and cervical spine injuries were most common (59%) and predictive of mortality (relative risk [RR], 2.19; interquartile range [IQR], 1.35-3.54; p = 0.001). Vehicle rollovers (RR, 2.75; IQR, 1.13-6.70; p = 0.01), vehicle ejection (RR, 4.18; IQR, 1.70-10.32; p = 0.000), alcohol intake (RR, 2.33; IQR, 1.52-3.56; p = 0.000), helmet use (RR, 1.82; IQR, 1.11-3.02; p = 0.01), and incident location were predictive of mortality. CONCLUSIONS: Increasing rates of ATV-related serious trauma and death are described in young males riding without helmets after consuming alcohol. Serious injuries contributed to healthcare costs in excess of $6.5 million USD. Predictors of mortality include rider behaviors and mechanical factors. Prevention should include rider education and industry measures to improve ATV stability.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".