Canadian Update on Surgical Procedures (CUSP) Urology Group consensus for intraoperative hemostasis during minimally invasive partial nephrectomy
Bibliographic record
Abstract
INTRODUCTION: Partial nephrectomy remains the gold standard in the management of small renal masses. However, minimally invasive partial nephrectomy (MIPN) is associated with a steep learning curve, and optimal, standardized techniques for time-efficient hemostasis are poorly described. Given the relative lack of evidence, the goal was to describe a set of actionable guiding principles, through an expert working panel, for urologists to approach hemostasis without compromising warm ischemia or oncological outcomes. METHODS: A three-step modified Delphi method was used to achieve expert agreement on the best practices for hemostasis in MIPN. Panelists were recruited from the Canadian Update on Surgical Procedures (CUSP) Urology Group, which represent all provinces, academic and community practices, and fellowship-and non-fellowship-trained surgeons. Thirty-two (round 1) and 46 (round 2) panellists participated in survey questionnaires, and 22 attended the in-person consensus meeting. RESULTS: An initial literature search of 945 articles (230 abstracts) underwent screening and yielded 24 preliminary techniques. Through sequential survey assessment and in-person discussion, a total of 11 strategies were approved. These are temporally distributed prior to tumor resection (five principles), during tumor resection (two principles), and during renorrhaphy (four principles). CONCLUSIONS: Given the variability in tumor size, depth, location, and vascularity, coupled with limitations of laparoscopic equipment, achieving consistent hemostasis in MIPN may be challenging. Despite over two decades of MIPN experience, limited evidence exists to guide clinicians. Through a three-step Delphi method and rigorous iterative review with a panel of experts, we ascertained a guiding checklist of principles for newly beginning and practicing urologists to reference.
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 | 0.028 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".