Comparison of avalanche survival patterns in Canada and Switzerland
Bibliographic record
Abstract
BACKGROUND: Current recommendations for rescue and resuscitation of people buried in avalanches are based on Swiss avalanche survival data. We analyzed Canadian survival patterns and compared them with those from Switzerland. METHODS: We extracted relevant data for survivors and nonsurvivors of complete avalanche burials from Oct. 1, 1980, to Sept. 30, 2005, from Canadian and Swiss databases. We calculated survival curves for Canada with and without trauma-related deaths as well as for different outdoor activities and snow climates. We compared these curves with the Swiss survival curve. RESULTS: A total of 301 people in the Canadian database and 946 in the Swiss database met the inclusion criteria. The overall proportion of people who survived did not differ significantly between the two countries (46.2% [139/301] v. 46.9% [444/946]; p = 0.87). Significant differences were observed between the overall survival curves for the two countries (p = 0.001): compared with the Swiss curve, the Canadian curve showed a quicker drop at the early stages of burial and poorer survival associated with prolonged burial. The probability of survival fell quicker with trauma-related deaths and in denser snow climates. Poorer survival probabilities in the Canadian sample were offset by significantly quicker extrication (median duration of burial 18 minutes v. 35 minutes in the Swiss sample; p < 0.001). INTERPRETATION: Observed differences in avalanche survival curves between the Canadian and Swiss samples were associated with the prevalence of trauma and differences in snow climate. Although avoidance of avalanches remains paramount for survival, the earlier onset of asphyxia, especially in maritime snow climates, emphasizes the importance of prompt extrication, ideally within 10 minutes. Protective devices against trauma and better clinical skills in organized rescue may further improve survival.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".