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Record W2802176236 · doi:10.1177/0967772017752897

Medical response to the declaration of the First World War: The case of Edwin Seaborn

2018· article· en· W2802176236 on OpenAlexaffabout
Alexandra C. Istl, Vivian C. McAlister

Bibliographic record

VenueJournal of Medical Biography · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicMedical History and Innovations
Canadian institutionsLondon Health Sciences CentreWestern University
Fundersnot available
KeywordsCentennialOfficerWorld War IISpanish Civil WarDeclarationUnit (ring theory)Government (linguistics)LawFirst world warWork (physics)Vietnam WarMedicineManagementPolitical scienceHistoryEngineeringPsychologyAncient history

Abstract

fetched live from OpenAlex

At the turn of the 20th century, Dr Edwin Seaborn was starting his surgical and academic career at Western University in Ontario. When war was declared in 1914, Seaborn prevailed upon the university's president to offer the Canadian government a fully staffed hospital for deployment overseas. Initially declined by the War Office in Ottawa, the university's offer was later accepted after mounting casualties stretched the capacity of the Canadian Army Medical Corps, and Seaborn was granted command of the new No. 10 Canadian Stationary Hospital. From 1916 to 1919, Seaborn's medical, surgical, and administrative practices transformed the humble No. 10 Stationary Hospital into a General Hospital that was indispensable to the war effort and raised the standard for military medical practice. Upon the unit's return to London, Ontario, Seaborn's dedication was transferred to his extensive work as an author, historian, academic, and beloved physician. During the centennial of the First World War, this paper explores the impact of an academic medical unit by looking at the career of its Commanding Officer: a man who made an invaluable contribution to the Canadian war effort and set a precedent for exceptional medical care at home and at war.

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)0.006
metaresearch head score (Gemma)0.002
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.339
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.003
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.032
GPT teacher head0.270
Teacher spread0.238 · 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 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

Citations1
Published2018
Admission routes2
Has abstractyes

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