History of the Department of Surgery at the University of Toronto: celebrating a centennial of progress and innovation
Bibliographic record
Abstract
Now in its centennial year since inauguration, the Department of Surgery at the University of Toronto lays claim to more than 500 faculty, 270 residents, and 250 clinical fellows. There are 7 direct entry residency training programs, and 4 subspecialty programs accredited by the Royal College of Physicians and Surgeons of Canada. There have been 10 chairs of the department since 1921. This article chronicles the life and times of the previous chairs in sequence; the success of the department originates from its many talented and luminary surgeons who have innovated and shaped their fields of surgery. In recent years, the department's academic productivity has been characterized by more than 1400 peer-reviewed publications per year, and annual research grant capture in excess of $90 million. Since the time of William Gallie, surgical trainees have been enabled to develop careers in surgery and science through the Gallie Program and, more recently, the Surgeon Scientist Training Program (SSTP) to attain higher graduate degrees. Providing quaternary surgical care at multiple hospital sites in Toronto, the Department of Surgery takes great pride in its robust clinical fellowship programs across all specialties that continue to attract trainees from around the world.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.016 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".