Maude Abbott and the Origin and Mysterious Disappearance of the Canadian Medical War Museum
Bibliographic record
Abstract
CONTEXT.—: In the early 1900s, it was common practice to retain, prepare, and display instructive pathologic specimens to teach pathology to medical trainees and practitioners; these collections were called medical museums. Maude Abbott, MD, established her reputation by developing expertise in all aspects of medical museum work. She was a founder of the International Association of Medical Museums (later renamed the International Academy of Pathology) and became an internationally renowned expert on congenital heart disease. Her involvement in the Canadian Medical War Museum (CMWM) is less well known. OBJECTIVE.—: To explore Abbott's role in the development of the CMWM during and after World War I and to trace its history. DESIGN.—: Available primary and secondary historical sources were reviewed. RESULTS.—: Instructive pathologic specimens derived from Canadian soldiers dying during World War I were shipped to the Royal College of Surgeons in London, which served as a clearinghouse for museum specimens from Dominion forces. The Canadian specimens were repatriated to Canada, prepared by Abbott, and displayed at several medical meetings. Abbott, because she was a woman, could not enlist and so she reported to a series of enlisted physicians with no expertise in museology. Plans for a permanent CMWM building in Ottawa eventually failed and Abbott maintained the collection at McGill (Montreal, Quebec, Canada) until her death in 1940. We trace the CMWM after her death. CONCLUSIONS.—: Sadly, after Abbott had meticulously prepared these precious teaching specimens so that their previous owners' ultimate sacrifice would continue to help their military brethren, the relics were bureaucratically lost.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.029 | 0.038 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".