Providing the Gift of Life: Canadian Medical Practitioners and the Treatment of Shock on the Battlefield
Bibliographic record
Abstract
The story of Ambroise Pare’s discovery has been told often; of how, during Francis I’s campaign against Turin in 1536-37, he ran out of the oil medical practitioners used to cauterize the stumps of amputees and used an herbal remedy and ligatures instead; and of how the patients treated by the latter method did so much better than those tortured with the former. The tale has much to commend it to the popular imagination: a medical hero makes a serendipitous discovery to relieve the suffering of thousands. However, the story is an exception to a steadfast rule in warfare, for in medical matters, change comes slowly. This state of affairs could be ascribed to an unthinking conservatism, but one should not rush to pass judgement. Military commanders are not so much muleheaded as wedded to techniques that, in their eyes, have worked well in the past; innovation means experiment, with perhaps catastrophic results. As we shall see in a study of how Canadian medical practitioners dealt with shock from the First World War to Korea, bringing about change is less a matter of conflict against the establishment and more of reaching a consensus on how to solve complex battlefield problems.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.039 | 0.022 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.011 | 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".