Pathology and Laboratory Medicine Support for the American Expeditionary Forces by the US Army Medical Corps During World War I
Bibliographic record
Abstract
CONTEXT: Historical research on pathology and laboratory medicine services in World War I has been limited. In the Spanish American War, these efforts were primarily focused on tropical diseases. World War I problems that could be addressed by pathology and laboratory medicine were strikingly different because of the new field of clinical pathology. Geographic differences, changing war tactics, and trench warfare created new issues. OBJECTIVES: To describe the scope of pathology and laboratory medicine services in World War I and the value these services brought to the war effort. METHODS: Available primary and secondary sources related to American Expeditionary Forces' laboratory services were analyzed and contrasted with the British and German approaches. RESULTS: The United States entered the war in April 1917. Colonel Joseph Siler, MD, a career medical officer, was the director, and Colonel Louis B. Wilson, MD, head of pathology at the Mayo Clinic, was appointed assistant director of the US Army Medical Corps Division of Laboratories and Infectious Disease, based in Dijon, France. During the next year, they organized 300 efficient laboratories to support the American Expeditionary Forces. Autopsies were performed to better understand treatment of battlefield injuries, effects of chemical warfare agents, and the influenza pandemic; autopsies also generated teaching specimens for the US Army Medical Museum. Bacteriology services focused on communicable diseases. Laboratory testing for social diseases was very aggressive. Significant advances in blood transfusion techniques, which allowed brief blood storage, occurred during the war but were not primarily overseen by laboratory services. CONCLUSIONS: Both Siler and Wilson received Distinguished Service Medals. Wilson's vision for military pathology services helped transform American civilian laboratory services in the 1920s.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.012 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".