Helicopter Rescue at Very High Altitude: Recommendations of the International Commission for Mountain Emergency Medicine (ICAR MedCom) 2025
Bibliographic record
Abstract
McLaughlin, Kyle, Charley Shimanski, Ken Zafren, Ian Jackson, Gerold Biner, Maurizio Folini, Andreas Hermansky, Eric Ridington, Peter Hicks, Giacomo Strapazzon, Marika Falla, Alastair Hopper, Dave Weber, Ryan Jackson, and Hermann Brugger. Helicopter rescue at very high altitude: recommendations of the International Commission for Mountain Emergency Medicine (ICAR MedCom) 2025. High Alt Med Biol. 27:107–116, 2026. Background: Helicopter mountain rescue is highly effective. However, air rescue operations at very high altitude (VHA), above 3500 m, are inherently hazardous because of challenging topography, dynamic weather, and hypobaric hypoxic environment. Currently, no established international guidelines exist for helicopter rescues at VHA. Methods: We conducted a narrative review of existing literature, supplemented by expert opinions from helicopter pilots, physicians, and rescuers actively engaged in VHA rescues. These recommendations were approved by the International Commission of Alpine Rescue (ICAR) Medical Commission and endorsed by the ICAR Air Rescue Commission. Discussion: We identified 19 key factors to consider, including operational parameters, ground site management, pilot and rescuer safety protocols, and psychological factors to assist air rescue programs in developing pre-mission, mission, and post-mission plans for helicopter rescue missions at VHA. Conclusions: By implementing the recommendations outlined in this article, helicopter rescue teams operating at VHA can improve response capabilities and minimize risks.
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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 | 0.023 | 0.060 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".