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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".