Bibliographic record
Abstract
When asked by The Canadian Journal of Urology to tell “my story” for the Legends in Urology section of this journal, I was of course flattered, surprised, and reminded that though I am seemingly the same person I was years ago, I am trapped in a still well-functioning body that nevertheless is 70 years old according to my birth certificate. I echo many of the “old” (and more qualified) “legends” that have preceded me: I am by almost every definition not a “legend.” Yet this mistaken selection does give me an opportunity to say a little about my career, in order to convey what I think I have learned during this long tenure in academia, and then end with some speculations about urology’s future. I have chosen not to recount the details of my professional journey; they have been previously published.1 Briefly, I began my post medical school laboratory and clinical training at NIH and Duke, intent on a career in CV surgery. My switch to urology was based on many reasons, but the most pertinent one was that I saw urology as being more conducive to an enduring career in academic surgery and laboratory research. Thanks in large part to the encouragement of Dave Paulson, soon to be the Chief of Urology at Duke, I transferred to the University of Minnesota under the direction of a new chair, Elwin Fraley. Dr. Paulson had previously worked with Dr. Fraley for 3 years at NIH. At the University of Minnesota, I was privileged to work in the laboratory of Dr. Tom Hakala and took over that lab when he moved to become chair of the University of Pittsburg. Soon after, I began an association with a tumor biologist, Robert Vessella, PhD, that now has lasted over 32 years. Increasingly, under his direction, the laboratory endured and flourished. We were fortunate to be continuously federally funded throughout this time. As I will explain subsequently, almost all of the “advances” that I was involved in were directly or indirectly due to this association.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.202 | 0.085 |
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".