Bibliographic record
Abstract
Introduction and Objectives:The introduction and advancement of minimally invasive surgery (MIS) has resulted in a reciprocal decline in exposure to open surgery during urology residency training.We propose organ procurement surgery as a potential vehicle to facilitate an increase in open surgical experience among trainees.We aimed to determine the surgical case volume for deceased organ procurement surgeries currently performed by urology residents in Canada, and determine what capacity exists for expansion regarding this procedure.Methods: Data on trainee participation in deceased organ procurement surgeries was extracted from Canadian urology residents case-logs between 2005 and 2009.Case-logs were derived from the voluntary self-reporting program T-RES®, anonymized, extracted, and analyzed.National deceased organ donor data were obtained from the Canadian Institute for Health Information.Results: The graduating Canadian urology resident performs on average less than one (0.95) organ procurement surgeries during 5 years of training.During the same period an average of 470 annual organ procurement surgeries were performed in Canada.The theoretical capacity exists for each graduating resident to perform an additional 16.3 multi-organ procurements during residency.Conclusions: With the establishment of MIS as standard of care for many urologic surgeries, the resultant decrease in open operative experience for urology residents is of concern.Innovative ways to enrich open surgical experience may be required.Canadian Urology residents do not appear to participate substantially in multi-organ procurement.Formal incorporation of rotations in deceased multi-organ procurement into urology residency training curriculum would substantially increase trainee experience in major open abdominal surgeries.
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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 | 0.011 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.049 | 0.015 |
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".