Organ procurement surgery as a means of increasing open surgical experience during urology residency
Bibliographic record
Abstract
INTRODUCTION: 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 define the surgical case volume for organ procurement surgeries currently performed by urology residents in Canada, and determine what capacity exists for expansion. METHODS: Data on organ procurement surgeries were extracted for Canadian urology residents case-logs between 2005 and 2009. Case-logs were anonymously analyzed through the voluntary self-reporting program T-Res (Resilience Software Inc.). National deceased organ donor data were obtained from the Canadian Institute for Health Information. RESULTS: The graduating Canadian urology resident has performed an average of 0.95 organ procurement surgeries during 5 years of training. An average of 469.6 procurement surgeries were performed yearly in Canada between 2005 and 2009. The theoretical capacity exists for each graduating resident to perform an additional 16.3 organ procurements during residency. CONCLUSIONS: With the establishment of MIS as standard of care for many urologic surgeries, the decrease in open operative experience is concerning. Innovative ways to enrich open surgical experience may be required, and increased formal incorporation of organ procurements into urology residency training curriculum may help fill the void.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".