Kurt Aterman, MUDR, MB, BCh BAO HONS, DCH, MRCP, PhD, DSc, FRCPath: “A Small Man With a Very Large Cerebrum and a Soul to Match”
Bibliographic record
Abstract
Kurt Aterman was raised in the Czech-Polish portions of the former Austro-Hungarian Empire during World War I and the interwar period. After completing medical school and beginning postgraduate pediatrics training in Prague, this Jewish Czech physician fled to England as a refugee when the Nazis occupied his homeland in 1939. He repeated/completed medical training in Northern Ireland and London, working briefly as a pediatrician. Next, he served in the Royal Army Medical Corp in India, working as a pathologist. After the war and additional pathology training, he spent the next decade as an experimental pathologist in Birmingham, England. After completing a fellowship with Edith Potter in Chicago, Aterman spent the next 2 decades as a pediatric-perinatal pathologist, primarily working in Halifax, Canada. Fluent in many European languages, he finished his career as a medical historian. Aterman published extensively in all 3 arenas; many of his pediatric pathology papers were massive encyclopedic review articles, accurately recounting ideas from historical times. Aterman was a classical European scholar and his papers reflected this. Aterman was one of the founding members of the Pediatric Pathology Club, the predecessor of the Society for Pediatric Pathology. This highly successful refugee's writings are important and memorable.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.038 | 0.014 |
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".