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Record W2777437011 · doi:10.1503/cjs.016716

Treatment of enemy wounded: evidence from the No. 7 Canadian Stationary Hospital (Dalhousie University)

2017· article· en· W2777437011 on OpenAlexaffvenueabout
Desmond Leddin, Paul Charlebois

Bibliographic record

VenueCanadian Journal of Surgery · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicMedical History and Innovations
Canadian institutionsCanadian Armed ForcesDalhousie University
Fundersnot available
KeywordsGermanMedicineFront (military)AdversaryWork (physics)Unit (ring theory)World War IIUniversity hospitalFirst world warHealth careMedical emergencyLawAncient historyHistoryPolitical scienceMeteorologyArchaeology

Abstract

fetched live from OpenAlex

SUMMARY: Dalhousie University, with the help of the other Maritime universities formed and sent a hospital to Europe during the First World War (WWI). They served from January 1916 to April 1919. There is no comprehensive account of the treatment of German wounded by Canadian Medical Services in WWI; however, there is direct photographic and written evidence from the No. 7 Canadian Stationary Hospital that the relationship was one of mutual trust, more characteristic of that between a health care provider and patient than between combatants. The activities of the No. 7 in treating German wounded from the Western Front provide insight into this undocumented aspect of the medical services in WWI. A previously unrecognized painting by Sir William Orpen, one of the leading artists of the 20th century, of the unit at work in France is described. An appendix to this commentary is available at canjsurg.ca.

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)0.005
metaresearch head score (Gemma)0.035
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 score0.327
Threshold uncertainty score0.657

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.035
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.004
Science and technology studies0.0030.002
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0130.001

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 head0.102
GPT teacher head0.220
Teacher spread0.118 · 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

Citations1
Published2017
Admission routes3
Has abstractyes

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