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Record W4223596421 · doi:10.1016/j.wem.2022.02.005

Death After Crevasse Rescue in Antarctica

2022· article· en· W4223596421 on OpenAlexaff
Gordon G. Giesbrecht, Jeffrey R. Brock

Bibliographic record

VenueWilderness and Environmental Medicine · 2022
Typearticle
Languageen
FieldMedicine
TopicThermal Regulation in Medicine
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsCrewCrevasseAeronauticsCardiopulmonary resuscitationMedical emergencyMeteorologyMedicineEmergency medicineEngineeringResuscitationPhysics

Abstract

fetched live from OpenAlex

We present a case report of a helicopter pilot who fell into a crevasse during a fuel delivery in Antarctica. He was trapped alone in the crevasse for 3 h while waiting for a rescue team to arrive, and a further 1 h during the extrication process. His condition deteriorated during the extrication and he lost consciousness and signs of life minutes after being dragged over the lip of the crevasse. He was then loaded into the rescue helicopter and treated with intermittent cardiopulmonary resuscitation during the 39-min return flight. Initial esophageal temperature on arrival at the Davis Base medical facility was 24.2°C. After 18 h of further treatment (mechanical ventilation with warm humidified O 2 , with internal and external warming) he was pronounced dead. The cause of death was hypothermia with minimal physical injury. This case highlights some of the extra challenges facing operational, rescue, and medical personnel in an isolated location. These complications include the tendency for flight crew to remove cold weather clothing during flight due to restricted mobility and excessive heat load from cabin heating; extended time for arrival of the rescue crew; extrication in a confined space; limited helicopter cabin space for transporting the rescue team and their rescue and medical equipment; and extended transport time to the nearest medical facility.

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.

How this classification was reachedexpand

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.997

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.010
GPT teacher head0.239
Teacher spread0.229 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2022
Admission routes1
Has abstractyes

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