Zero Ischemia Robotic-Assisted Partial Nephrectomy in Alberta: Initial Results of a Novel Approach
Bibliographic record
Abstract
INTRODUCTION: Partial nephrectomy remains the standard of care in early stage, organ-confined renal tumours. Recent evidence suggests that minimally invasive surgery can proceed without segmental vessel clamping. In this study, we review our experience at a Canadian centre with zero ischemia robotic-assisted partial nephrectomy (RAPN). METHODS: A retrospective chart review of zero ischemia RAPN was performed. All surgeries were consecutive partial nephrectomies performed by the same surgeon at a tertiary care centre in Northern Alberta. The mean follow-up period was 28 months. These outcomes were compared against the current standards for zero ischemia (as outlined by the University of Southern California Institute of Urology [USC]). RESULTS: We included 21 patients who underwent zero ischemia RAPN between January 2012 and June 2013. Baseline data were similar to contemporary studies. Twelve (57.1%) required no vascular clamping, 7 (33.3%) required clamping of a single segmental artery, and 2 (9.5%) required clamping of two segmental arteries. We achieved an average estimated blood loss of 158 cc, with a 9.2% average increase in creatinine postoperatively. Operating time and duration of hospital stay were short at 153 minutes and 2.2 days, respectively. CONCLUSION: Zero ischemia partial nephrectomy was a viable option at our institution with favourable results in terms of intra-operative blood loss and postoperative creatinine change compared to results from contemporary standard zero ischemia studies (USC). To our knowledge, this is the first study to review an initial experience with the zero ischemia protocol in robotic-assisted partial nephrectomies at a Canadian hospital.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".