Changing of the guard? A glance at the surgical representation in the Canadian renal transplantation community
Bibliographic record
Abstract
INTRODUCTION: Renal transplant is the gold standard treatment for end-stage renal disease (ESRD), and the prevalence of both ESRD and renal transplant has been steadily increasing over the past decade. However, involvement of urology in renal transplant has been declining. We examine the current state of urology involvement in renal transplant programs across Canada. METHODS: A telephone survey of all surgical transplant centres in Canada was performed. Information regarding the number of transplant surgeons, their individual training background, and their involvement in specific procedures, including open and laparoscopic living donor nephrectomy, deceased donor nephrectomy, and recipient renal transplant were collected. RESULTS: There are 59 Canadian transplant surgeons, including 27 (46%) who completed a urology residency and 32 (54%) with a general surgery background. With regards to procedures performed, 58 (98%) perform recipient renal transplant surgery, 36 (61%) perform laparoscopic donor nephrectomy, and 17 (29%) perform open donor nephrectomy. There was no significant difference in the number of surgeons that perform renal recipient surgery, laparoscopic or open donor nephrectomies, and deceased donor nephrectomies between surgeons of the two different training backgrounds. CONCLUSIONS: The role of urology in Canadian renal transplant has declined significantly over the past decade. Given the medical and surgical complexity of renal transplant, along with the growing need for renal transplants, a multidisciplinary team approach is imperative. Strong urology involvement with the transplant team is crucial for optimal care of these complex patients.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.016 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.000 |
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".