The Prince Edward Island–Mayo Clinic Connection: Malcolm B. Dockerty and Lewis B. Woolner
Bibliographic record
Abstract
CONTEXT: Malcolm B. Dockerty and Lewis B. Woolner, 2 preeminent mid-20th-century surgical pathologists, spent their entire careers at the Mayo Clinic. Both were raised in poverty on potato farms only 49 miles apart in Canada's smallest province (Prince Edward Island); both were educated in 1-room schools and graduated as gold medalists from Prince Edward Island's only college and then from Maritime Canada's only medical school; both then trained at the Mayo Clinic. OBJECTIVE: To explore the lives and accomplishments of these 2 important surgical pathologists. DESIGN: Standard historiographic methods were used to explore primary and secondary historical sources. RESULTS: Both became world-renowned general surgical pathologists, one developing subspecialty expertise in gynecologic pathology and the other in cytopathology, pulmonary pathology, and thyroid/parathyroid pathology. Both were prolific authors with h-indices higher than 40, and between them, they published more than 750 peer-reviewed papers and book chapters. As educators, they trained hundreds of pathology and surgery residents/fellows who disseminated their knowledge around the world. Both were fascinated by poetry from childhood and could quote the classics from memory. One wrote poetry throughout his entire life and even used it to teach pathology and serve as his memoir; the other strongly preferred the classics and in jest called his colleague "a (minor) poet." Both received postretirement honorary doctorates from their alma maters. Dockerty died in 1987; Woolner celebrates his 100th birthday on November 17, 2013. CONCLUSION: Every pathologist should know of these 2 pioneering surgical pathologists.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.058 | 0.011 |
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".