Patterns of death among avalanche fatalities: a 21-year review
Bibliographic record
Abstract
BACKGROUND: Avalanches are a significant cause of winter recreational fatalities in mountain regions. The purpose of this study was to determine the relative contributions of trauma and asphyxia to avalanche deaths. METHODS: We reviewed all avalanche fatalities between 1984 and 2005 that had been investigated by the offices of the British Columbia Coroners Service and the Chief Medical Examiner of Alberta. In addition, we searched the database of the Canadian Avalanche Centre for fatal avalanche details. We calculated injury severity scores for all victims who underwent autopsy. RESULTS: There were 204 avalanche fatalities with mortality information over the 21-year study period. Of these, 117 victims underwent autopsy, and 87 underwent forensic external examination. Asphyxia caused 154 (75%) deaths. Trauma caused 48 (24%) deaths, with the rate of death from trauma ranging from 9% (4/44) for snowmobilers to 42% (5/12) for ice climbers. In addition, 13% (12/92) of the asphyxia victims who underwent autopsy had major trauma, defined as an injury severity score of greater than 15. Only 48% (23/48) of victims for whom trauma was the primary cause of death had been completely buried. INTERPRETATION: Asphyxia and severe trauma caused most avalanche fatalities in western Canada. The relative rates differed between snowmobilers and those engaged in other mountain activities. Our findings should guide recommendations for safety devices, safety measures and resuscitation.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".