Management of Multi-Casualty Incidents in Mountain Rescue: Evidence-Based Guidelines of the International Commission for Mountain Emergency Medicine (ICAR MEDCOM)
Bibliographic record
Abstract
Blancher, Marc, François Albasini, Fidel Elsensohn, Ken Zafren, Natalie Hölzl, Kyle McLaughlin, Albert R. Wheeler III, Steven Roy, Hermann Brugger, Mike Greene, and Peter Paal. Management of multi-casualty incidents in mountain rescue: Evidence-based guidelines of the International Commission for Mountain Emergency Medicine (ICAR MEDCOM). High Alt Med Biol. 19:131-140, 2018. INTRODUCTION: Multi-Casualty Incidents (MCI) occur in mountain areas. Little is known about the incidence and character of such events, and the kind of rescue response. Therefore, the International Commission for Mountain Emergency Medicine (ICAR MEDCOM) set out to provide recommendations for the management of MCI in mountain areas. MATERIALS AND METHODS: Details of MCI occurring in mountain areas related to mountaineering activities and involving organized mountain rescue were collected. A literature search using (1) PubMed, (2) national mountain rescue registries, and (3) lay press articles on the internet was performed. The results were analyzed with respect to specific aspects of mountain rescue. RESULTS: We identified 198 MCIs that have occurred in mountain areas since 1956: 137 avalanches, 38 ski lift accidents, and 23 other events, including lightning injuries, landslides, volcanic eruptions, lost groups of people, and water-related accidents. DISCUSSION: General knowledge on MCI management is required. Due to specific aspects of triage and management, the approach to MCIs may differ between those in mountain areas and those in urban settings. CONCLUSIONS: Mountain rescue teams should be prepared to manage MCIs. Knowledge should be reviewed and training performed regularly. Cooperation between terrestrial rescue services, avalanche safety authorities, and helicopter crews is critical to successful management of MCIs in mountain areas.
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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 | 0.029 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.017 | 0.009 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".