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Record W2046068059 · doi:10.1089/ham.2013.1069

Mount Everest and Makalu Cold Injury Amputation: 40 Years On

2014· article· en· W2046068059 on OpenAlexaff
Shawnda A. Morrison, Jurij Gorjanc, Igor B. Mekjavić

Bibliographic record

VenueHigh Altitude Medicine & Biology · 2014
Typearticle
Languageen
FieldMedicine
TopicExercise and Physiological Responses
Canadian institutionsAcadia University
Fundersnot available
KeywordsFrostbiteMedicineAmputationFoot (prosody)Cold injuryExtreme ColdNumerical digitSurgeryCold stressStress fractures

Abstract

fetched live from OpenAlex

Freezing cold injuries (frostbite) of the extremities are a common injury among alpinists participating in high altitude expeditions, particularly during inclement weather conditions. Anecdotally, a digit that has suffered frostbite may be at greater risk to future cold injuries. In this case study, we profile a 62-year-old elite alpinist who suffered multiple digit amputations on both his hands and foot after historic summit attempts on Makalu (8481 m) and Mt. Everest (8848 m) in 1974-1979. We describe the clinical treatment he received at that time, and follow up his case 40 years after the first incidence of frostbite utilizing a noninvasive evaluation of hand and foot function to a cold stress test, including rates of re-warming to both injured and non-injured digits. Finger rates of recovery to the cold stress test were not different (0.8 vs. 1.0°C·min(-1)) except one (injured, left middle finger, distal phalanx; 0.4°C·min(-1)). Toe recovery rates after cold-water immersion were identical between previously injured and non-injured toes (0.2°C·min(-1)). Thermocouple data indicate that this alpinist's previous frostbite injuries may not have significantly altered his digit rates of re-warming during passive recovery compared to his non-injured digits.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.706
Threshold uncertainty score0.470

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.017
GPT teacher head0.315
Teacher spread0.298 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations10
Published2014
Admission routes1
Has abstractyes

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