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Record W3024490327

[From war to priority battle; blood transfusion as a medical innovation during World War I].

2017· article· en· W3024490327 on OpenAlexaboutno aff
Ton van Helvoort

Bibliographic record

VenuePubMed · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicMedical History and Innovations
Canadian institutionsnot available
Fundersnot available
KeywordsBattleMedicineBlood transfusionShock (circulatory)World War IIScale (ratio)Intensive care medicineLawSurgeryAncient historyHistoryInternal medicinePolitical science
DOInot available

Abstract

fetched live from OpenAlex

The logistics system for blood transfusion was first developed on the Western Front during World War I. This article focuses on the people who played a major role in this development. It discusses the people who came up with the idea of preventing coagulation through addition of citrate and who discovered the stabilisation of blood by adding glucose. The inclusion of citrate can be regarded as having been simultaneously developed in several countries, while the stabilisation of erythrocytes was discovered by American researchers. As regards to the credit for being the first person to apply blood transfusion as a logistics system, this priority development was contested by an American and a Canadian, who coincidentally had the same surname - Robertson. The war induced both of them to start large-scale implementation of their discovery of blood transfusion. The Germans, however, generally continued with the traditional treatment for blood loss and shock by administering saline and gum arabic.

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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0030.004
Scholarly communication0.0040.004
Open science0.0010.002
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0130.004

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 head0.042
GPT teacher head0.244
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
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

Citations0
Published2017
Admission routes1
Has abstractyes

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