Bibliographic record
Abstract
he bulk of what community urologists see could be called "medical urology."We work through large numbers of patients each week -a week punctuated with stones, retention, and hematuria calls from our local hospital's emergency room.We have surgical interests as well and they eventually define our practice.My partner is a stone guy, another department member loves lap kidneys, and I actually like male incontinence.It is difficult for me to justify a broad range of surgical skills when my colleagues can do a better job.This applies to advanced surgical skills only, as you can only be so skilled at circumcisions, vasectomies, and hydroceles.There is good published data that suggests that high-volume surgeons have superior outcomes.This does not mean that low-volume guys don't as wellwell, some don't and we know that empirically.Where am I going with this?I'll use my experience as an example.In 1984, I started my practice in Oakville, Ontario.Patrick Walsh had just published his treatise on prostate cancer and nerve-sparing.Prior to this, radical prostatectomies were nightmarish procedures.I had just spent years at Toronto General Hospital learning the transpubic route to the radical prostatectomy.The only nerves spared were our own if we were able to finish in under three hours and not have to arrange for an ICU bed.Walsh's attention to detail and the introduction of prostate-specific antigen slowly changed this procedure, the patient mix, and our acceptance of the procedure.With a little hustle, I was able to convince a number of local urologists that I could handle the work, and by 1992, I was logging about 75 radical prostatectomies a year.It was a routine procedure, rarely requiring blood, with patients staying in hospital for two nights at most.My partner and I heard that Ralph Clayman had tried a few using a laparoscope and thought he was nuts.Fast-forward 15 years and few new grads are taught the classic Walsh prostatectomy.I had been too busy working and too narrow-minded to embrace the new technology.When Bobby Shaygean opened his robot for business, we decided to send everyone that way.We could not compete with his results and we owed it to our patients.
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 | 0.012 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.033 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.017 | 0.032 |
| Insufficient payload (model declined to judge) | 0.034 | 0.008 |
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".