Snowmobile trauma: 10 years' experience at Manitoba's tertiary trauma centre.
Bibliographic record
Abstract
INTRODUCTION: According to the literature, the increased recreational use of the snowmobile has resulted in an increasing number of musculoskeletal injuries. We wished to examine whether previously described risk factors continue to be associated with snowmobile trauma and to identify previously unrecognized risks and specific patterns of injury. METHODS: We carried out a chart review of all snowmobile-related injuries over a 10-year period at the Health Sciences Centre in Winnipeg, the only level 1 trauma centre serving the Province of Manitoba, with particular attention to the risk factors of suboptimal lighting, excessive speed and alcohol consumption. RESULTS: We identified 480 injuries in 294 patients, and 81 (27.6%) of these patients died. Collisions accounted for 72% of the injury mechanisms. Of the injuries sustained, 31% occurred on roads. Excessive speed was a risk factor in 54% of patients, suboptimal lighting in 86% and a blood alcohol level greater than 0.08 in 70%. Musculoskeletal injuries accounted for 57% of those recorded. There were also brachial plexus injuries (3%) and knee dislocations (2%). To our knowledge, this is the largest study detailing injury associated with recreational use of snowmobiles in Canada. CONCLUSIONS: Because snowmobile trauma is caused principally by human errors, it is potentially preventable. Efforts aimed at prevention must focus on the driver, who controls the common risk factors. The danger of snowmobiling while intoxicated must be emphasized. Trail-side monitoring is likely to be ineffective, as the majority of accidents do not occur on designated snowmobile trails.
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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) | 0.001 | 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".