Retrospective review of all-terrain vehicle accidents in Alberta
Bibliographic record
Abstract
BACKGROUND: All-terrain vehicles (ATVs) are frequently associated with injuries and deaths. In spite of this, very few guidelines, let alone legal restrictions, exist to guide users of these machines. METHODS: We conducted a standardized review of prospectively collected data from the Alberta Trauma Registry. All patients who were involved in ATV-related traumas from 2003 to 2008 with an Injury Severity Score (ISS) greater than 12 were included. The variables studied were age, sex, type of vehicle, purpose of use, person injured (driver or passenger), ISS, distribution of injuries, length of hospital stay, helmet use and death. RESULTS: We evaluated 435 patients with ATV-related injuries and ISS greater than 12. The average ISS was 22.8, with an overall mortality of 4.6%; 55% of patients were not wearing helmets, and most of the deaths (85%) occurred among these individuals. Helmet use was associated with a lower risk of mechanical ventilation and of injury to the head and/or cervical spine. Children accounted for 18.9% of all patients and 15% of deaths; 57% of them were wearing helmets at the time of their accidents. CONCLUSION: All-terrain vehicle use in Alberta carries a significant risk of injury and death, and there is an association between death and lack of helmet use. A minimum age for ATV use of at least 16 years and a legal requirement for helmet use may increase public awareness of these risks and decrease morbidity and mortality.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.011 | 0.018 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".