Bibliographic record
Abstract
In august 1914, at the start of World War I, blood transfusion remains quite infrequent, with rough methods, inaccurate indications and poor results. The direct surgical techniques of arteriovenous anastomosis proved ill-adapted to the emergency conditions of war wounds. Indirect techniques with syringes and storage tubes were frequently limited, and complicated, by blood-clotting. Moreover, despite Landsteiner's discovery of ABC blood groups in 1901, compatibility testing was poorly known and often considered unnecessary. At the beginning of the war, none of the belligerent armies'medical services was specifically organized for blood transfusion. In the early years of the war (1914-1916), blood transfusions remain rare. The first transfusion in the French army was performed by Emile Jeanbrau on 16 October 1914. The main impulse, however, came from surgeons of the Canadian Army Medical Corps (CAMC), who had learned about transfusion from doctors in the United States (Bruce Robertson, Edward Archibald). Transfusions became increasingly frequent, particularly as part of pre-operative preparation in cases of wound shock and hemorrhage. The last years (1917-1918) were marked by the arrival of the American Army in France, with a growing medical influence of American doctors. Oswald Robertson introduced the use of citrated blood in glass bottles, being subsequently called "the first blood banker". Blood transfusion remained throughout the war infrequent and technically imperfect. Wartime, however, by the efforts of some young Canadian and American doctors, was a tremendous opportunity for diffusion and improvement.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".