Doceo ergo sum: mentoring surgeons
Bibliographic record
Abstract
The Canadian Journal of Surgery (CJS) mourns the recent deaths of 3 surgical masters by celebrating their lives as teachers. Jean Couture (1924–2016) was chairman of the department of surgery at Université Laval, where he mentored generations of surgeons.1 Dr. Couture led and taught nationally through organizations such as the Canadian Association of General Surgeons and the Royal College of Physicians and Surgeons of Canada, which he served in many capacities, including as president of both organizations. In the 1990s, when the future of CJS was uncertain, he intervened to bring in the support, financial and academic, of specialty societies. Don Wilson (1917–2017), chairman of the Department of Surgery at the University of Toronto, was also a president of the Royal College. Dr. Wilson was a pioneer of bioethics in Canada, campaigning for its integration into every aspect of specialty training. Tom McLarty (1925–2017) was neither chairman of a department nor president of a national organization, but he inspired love and gratitude among generations of surgeons in southwestern Ontario. His gentle “let me show you how to do that” approach fostered technical excellence and an open mind to innovation among his devoted followers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.400 | 0.179 |
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".