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Ambulance Trains—From the Crimean War to Ukraine

2023· article· en· W4381715197 on OpenAlexaff
Sanders Marble, Justin Barr

Bibliographic record

VenueJAMA Network Open · 2023
Typearticle
Languageen
FieldMedicine
TopicTravel-related health issues
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsTrainAeronauticsGeographyPolitical scienceEngineeringCartography

Abstract

fetched live from OpenAlex

When war erupted in Ukraine in 2022, hostilities compromised health care facilities near the front line.While the military evacuated uniformed personnel, civilians with injuries or other medical conditions also required treatment and transportation.This study by Walravens et al 1 describes how Médecins Sans Frontières (MSF), the Ukrainian Railway Company, and the Ukrainian Ministry of Health collaborated to retrofit 2 trains into rail-ambulances, removing nearly 2500 civilian patients over 8 months.The study 1 details the construction of the cars, both a low-acuity conveyance and later an intensive care-capable train; the logistics of transit; and relates the numbers and types of patients transported, noting that patients with trauma injuries predominated early in the conflict, after which patients with medical and humanitarian concerns became the majority.Medical evacuation by train synchronizes with their wartime introduction.In 1854, the British and French built the first military rail line, linking the port of Sevastopol to Balaklava in Crimea. 2 Nearly 8 miles long, it carried more than 100 tons of material from the docks to the front each day and returned with hundreds of wounded patients, albeit initially only ambulatory patients.This early experiment demonstrated the value of railway ambulances: they provided relatively fast, safe, and mass evacuation of casualties.Ambulance trains (or hospital trains) have appeared in various incarnations in almost every major conflict since.With a robust rail network, both sides of the American Civil War depended heavily on hospital trains.3 The first such evacuation occurred after the battle at Wilson's Creek, Missouri, in August 1861.Initially, the Army utilized standard cattle and freight cars, with beds of straw or pine needles to cushion the ride.Early efforts to suspend stretchers from the ceiling proved ineffective and hazardous, given the swaying, jolting nature of the ride.By 1862, the Wilmington and Baltimore Railroad constructed the first purpose-built ambulance carriage: stretchers hung from rubber rings, which functioned as shock absorbers, and new doorways allowed medical personnel to maneuver among moving cars, facilitating patient care.Designs advanced over the course of the war, resulting in self-sufficient ambulance trains with kitchens, pharmacies, water supplies, and treatment rooms.Hospital trains soon crisscrossed the land and catalyzed the creation of large base hospitals caring for tens of thousands of patients.All belligerents in World War I relied heavily on ambulance trains, with the British deploying dozens overseas and more within the United Kingdom.Germany had a remarkable 238 such conveyances.4 Late entry allowed the US to learn from their allies, buying purpose-built British carriages.Hospital Train No. 18, for example, was comprised of approximately 16 cars, including an integral power source, staff quarters, kitchen, pharmacy, operating room, and multiple cars for patients in stretchers, ambulatory patients, or isolation patients.5 It could carry 570 patients comfortably, more when litters filled available spaces at the expense of enroute care.From July to September 1918, it evacuated more than 10 000 patients.Trains would try to coordinate with receiving hospitals to deposit all patients at 1 facility, telegraphing ahead the number, type, and severity of the casualties on board.6 World War II marked a similar pattern, with specialized cars replacing mostly improvised trains.Again entering late, the United States purchased British trains for use in Europe, built their own, and improvised from captured rolling stock.Following demobilization in 1945, the Ringling Brothers and Barnum & Bailey circuses acquired several cars for use in their circus trains.Trains continued to roll in Korea.Hastily deployed, the 21st and 22nd Army Hospital Train Detachments renovated Korean passenger coaches (dating from 1860) into litter-bearing carriages.

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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)0.002
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.153
Threshold uncertainty score0.303

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0040.001
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0050.001

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 head0.075
GPT teacher head0.376
Teacher spread0.301 · 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
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
Published2023
Admission routes1
Has abstractyes

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