Blood transfusion at the time of the First World War – practice and promise at the birth of transfusion medicine
Bibliographic record
Abstract
The centenary of the start of the First World War has stirred considerable interest in the political, social, military and human factors of the time and how they interacted to produce and sustain the material and human destruction in the 4 years of the war and beyond. Medical practice may appear distant and static and perhaps seems to have been somewhat ineffectual in the face of so much trauma and in the light of the enormous advances in medicine and surgery over the last century. However, this is an illusion of time and of course medical, surgical and psychiatric knowledge and procedures were developing rapidly at the time and the war years accelerated implementation of many important advances. Transfusion practice lay at the heart of resuscitation, and although direct transfusion from donor to recipient was still used, Geoffrey Keynes from Britain, Oswald Robertson from America and his namesake Lawrence Bruce Robertson from Canada, developed methods for indirect transfusion from donor to recipient by storing blood in bottles and also blood-banking that laid the foundation of modern transfusion medicine. This review explores the historical setting behind the development of blood transfusion up to the start of the First World War and on how they progressed during the war and afterwards. A fresh look may renew interest in how a novel medical speciality responded to the needs of war and of post-war society.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".