Bibliographic record
Abstract
I would like to thank The Canadian Journal of Urology for this kind invitation to contribute an article to the Legends in Urology section.While I have never considered myself to be in the category of legend, I greatly appreciate the opportunity to reflect on my career.In addition to telling a story, I will focus on a few themes including innovation, international education and medical leadership.I was born in what was a small town at the time, Brampton, Ontario on the fringes of Toronto.That small town is now over 600,000 people and part of the large urban area of Canada's largest city.My mother was a nurse and my father was an elementary school principal.When my Dad moved to this town in the mid 1950's from the family farm, he was the principal of the only school.I played little league baseball as a kid and although I thought I was a pretty good second baseman, my limited hitting skills positioned me as less than a legend in baseball.Aside from my mother, other connections to medicine in my family included a couple of uncles who were surgeons.I did not have a significant amount of contact with them but knew they were urology doctors, however I had little notion of what exactly that entailed.In fact, my great uncle, Dr. Lloyd McAninch, founded the urology residency training program at the University of Western Ontario in the 1950's and was himself a legend in urology locally, nationally and in the Northeastern Section of the AUA where he served as President in 1973-1974.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.191 | 0.053 |
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".