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Record W2807061814 · doi:10.1016/j.wem.2018.03.007

Wilderness Mass Casualty Incident (MCI): Rescue Chain After Avalanche at Everest Base Camp (EBC) In 2015

2018· article· en· W2807061814 on OpenAlex
Ken Zafren, Anne Brants, Katie Tabner, Andrew Nyberg, Matiram Pun, Buddha Basnyat, Monika Brodmann Maeder

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueWilderness and Environmental Medicine · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHigh Altitude and Hypoxia
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsWildernessMass-casualty incidentMedical emergencyMedicineAeronauticsPoison controlSuicide preventionEngineering

Abstract

fetched live from OpenAlex

The Nepal Earthquake of 2015 killed over 8000 people and injured over 20,000 in Nepal. Moments after the earthquake, an avalanche of falling ice came down from above Everest Base Camp (EBC). The air blast created by the avalanche flattened the middle part of EBC, killing 15 people and injuring at least 70. The casualties were initially triaged and treated at EBC and then evacuated by air to Kathmandu for definitive care. There were intermediate stops at the villages of Pheriche and Lukla during which the casualties were offloaded, retriaged, treated, and loaded again for further transport. Most of the authors of this article helped to provide primary disaster relief at EBC, Pheriche, or Lukla immediately after the earthquake. We describe the process by which an ad hoc rescue chain evacuated the casualties. We discuss challenges, both medical and nonmedical, what went well, and lessons learned. We make recommendations for disaster planning in the Khumbu (Everest) region, an isolated high altitude roadless area of Nepal.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.388
Threshold uncertainty score0.965

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.007
GPT teacher head0.231
Teacher spread0.224 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it