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Record W4377092804 · doi:10.1213/ane.0000000000006525

Anesthesia in the Korean War

2023· article· en· W4377092804 on OpenAlexaff
Elizabeth Zhao, Justin Barr

Bibliographic record

VenueAnesthesia & Analgesia · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicMedical History and Innovations
Canadian institutionsToronto General Hospital
Fundersnot available
KeywordsMedicineCurareDocumentationEndotracheal intubationWorld War IIAnesthesiaIntubationMilitary medicineFirst world warLawHumanities

Abstract

fetched live from OpenAlex

Relying on original, primary source documentation from the National Archives, we describe the practice of anesthesia in mobile army surgical hospital (MASH) units and the 171st Evacuation Hospital during the latter part of the Korean War in 1953. Values were scaled and reported as percentages. These Essential Technical Medical Data Sheets reveal a surprising proportion (12.9%) of men received spinal anesthetics, despite official recommendations to the contrary. Still, the majority (69.2%) of the wounded underwent general anesthesia, most commonly through a mixture of thiopental and nitrous oxide. Despite data from World War II demonstrating the advantages of endotracheal intubation in these patients, few patients (20.6%) were intubated. Six percent benefited from the new curare-based drugs. This is the first English-language article that describes the practice of anesthesia during the Korean War. Utilizing primary source documentation, we found that general anesthesia was the most common type utilized. Newer techniques were not as commonly adopted, despite official recommendations and data from the time. The care provided closely resembled that delivered in the Second World War but inspired a series of technological and pedagogical reforms through the 1950s to improve military anesthesia for the next conflict.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.040
GPT teacher head0.242
Teacher spread0.202 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2023
Admission routes1
Has abstractyes

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