Daniel Smith Lamb (1843–1929): A window into the early histories of the Army Medical Museum and Howard University Medical School
Bibliographic record
Abstract
U.S. Army doctor Daniel Smith Lamb was a significant figure in the history of American pathology during its formative years. For 55 years (1865-1920), Lamb performed hundreds of autopsies in and around Washington, D.C. and personally collected over 1,500 gross pathology specimens for the Army Medical Museum. His work began at the close of the Civil War and continued on through World War I, contributing substantially to gross pathological and histological studies that documented wartime pathology, thus further contributing to the training of Army doctors. Specimens he collected also include material from autopsies he conducted on President James Garfield, his assassin Charles Guiteau, and other historical figures. Under the auspices of the Army Medical Museum, he conducted autopsies across the city of Washington for the museum's collection, many of which survive to this day at the National Museum of Health and Medicine. He served under 12 U.S. Army Surgeons General and 11 Museum Curators and was noted to be a steadying influence during a time of constant leadership changes at that institution. Lamb was known throughout Washington, D.C. as an advocate of medical education for African-Americans and women. While working at the Museum, he simultaneously served for 46 years as professor of anatomy at Howard University (1877-1923). He wrote seminal histories of the institutions with which he was associated and in so doing also contributed significantly to the study of the history of medicine.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.012 | 0.013 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".