A Multi-year Review of Major Off-Road Vehicle Injuries and Deaths in New Brunswick
Bibliographic record
Abstract
INTRODUCTION: Off-road vehicles are widely used in Canada, especially in rural areas. Despite their popularity, there is limited data on off-road vehicle-related injuries, particularly in New Brunswick (NB). The aim of this study was to describe the epidemiology of off-road vehicle incidents in NB, focusing on frequency, severity, and factors associated with morbidity over an eight-year period (2014-2021). METHODS: We conducted a retrospective observational study using data from the New Brunswick Trauma Registry, which includes all patients admitted to level 1, 2, and 3 trauma centers with severe injuries. We analyzed data on demographics, incident characteristics, substance use, helmet use, and outcomes. Regression analyses assessed factors predicting intensive care unit admission and hospital length of stay. RESULTS: Between December 2014 and December 2021, 681 patients recorded in the trauma registry were involved in off-road vehicle incidents. The median age of these patients was 38.0 years, with 533 patients (78.3%) identified as male and 77 (11.3%) under 18 years old. All-terrain vehicles were involved in 456 (67%) of the incidents, while 225 (33%) involved snowmobiles. Among those tested, 176 (54.2%) had used alcohol, and 390 (57.3%) had used a helmet. The most frequent types of incidents included off-road travel incidents (43.3%) and rollovers (23.5%). The median Injury Severity Score was 13, and the median hospital length of stay was four days. Regression analysis showed that older age and intoxication were significant predictors of longer hospital stays and increased likelihood of admission to the intensive care unit. CONCLUSION: This study describes factors associated with off-road vehicle incidents in New Brunswick. Off-road vehicle trauma in New Brunswick presents a considerable public health challenge, with the male gender, older age, and substance use associated with increased morbidity. Effective injury prevention requires a multifaceted approach, including enhanced education, stricter regulations, and improved enforcement to mitigate the risks associated with off-road vehicle (ORV) use.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.018 | 0.028 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".