Transplantation and Surgery: A Discussion on the Current and Future Direction of Renal Transplantation
Bibliographic record
Abstract
Dr. Jeff Warren, MD, FRCPC, is an associate professor at the University of Ottawa within the Department of Surgery, Division of Urology. He has been a staff Urologist since 2009 and obtained his fellowship in multi-organ transplants, including kidneys and pancreases, from the University of Western Ontario. He received his MD from the University of Ottawa in 2002 and also completed his residency at the University of Ottawa in 2007. He is currently the head of surgical foundations for all surgical residency programs at the University of Ottawa. His clinical interests are in kidney transplantation surgery, minimally invasive surgery, and medical education. Dr. Tom Skinner, MD, FRCPC, is a transplant fellow at the University of Ottawa within the Department of Surgery, Division of Urology. He received his MD from Dalhousie University in 2012 and completed his Urology residency at Queen’s University in 2017. He has a BSc. from the University of British Columbia and a MSc. from McGill University. His clinical interests are in minimally invasive surgery, renal transplantation, surgical education, and healthcare economics. During this interview, Dr. Skinner and Dr. Warren discuss the current state of transplant surgery, the biggest challenges to transplanting patients, and the future of the specialty. They also discuss robotic surgery and the Spanish model for organ donation.
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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 | 0.025 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.012 | 0.020 |
| Scholarly communication | 0.014 | 0.031 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.030 | 0.043 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".