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Record W2790727753 · doi:10.1503/cjs.012717

No. 3 Canadian General Hospital (McGill) in the Great War: service and sacrifice

2018· article· en· W2790727753 on OpenAlexvenueaboutno aff
Andrew Beckett, Edward J. Harvey

Bibliographic record

VenueCanadian Journal of Surgery · 2018
Typearticle
Languageen
FieldMedicine
TopicHistory of Medical Practice
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineGeneral hospitalWorld War IISacrificeSpanish Civil WarUnit (ring theory)First world warGerontologyFamily medicineHistoryAncient historyArchaeologyPsychology

Abstract

fetched live from OpenAlex

SUMMARY: During the Great War, McGill University fielded a full general hospital to care for the wounded and sick among the Allied forces fighting in France and Belgium. The unit was designated No. 3 Canadian General Hospital (McGill) and included some of the best medical minds in Canada. Because the unit had a relationship with Sir William Osler, who was a professor at McGill from 1874 to 1885, the unit received special attention throughout the war, and legendary Canadian medical figures, such as John McCrae, Edward Archibald and Francis Scrimger, VC, served on its staff. The unit cared for thousands of victims of the war, and its trauma care advanced through the clinical innovation and research demanded by the nature of its work. Although No. 3 Canadian General Hospital suffered tragedies as well, such as the deaths of John McCrae and Osler's only son Revere, by the war's end the McGill hospital was known as one of the best medical units within the armies in France.

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)0.004
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.306
Threshold uncertainty score0.616

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.003
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0770.008

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 head0.030
GPT teacher head0.240
Teacher spread0.210 · 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

Citations3
Published2018
Admission routes2
Has abstractyes

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