Hilar control during laparoscopic donor nephrectomy: Practice patterns in Canada
Bibliographic record
Abstract
INTRODUCTION: In recent years, the method of vascular control during laparoscopic donor nephrectomy (LDN) has come under scrutiny due to catastrophic consequences of a device failure. This study sought to examine the surgical preferences of Canadian donor surgeons with regards to vascular control and their perception on the safety of these modalities. We also surveyed the experience with device malfunction and their subsequent management during LDN. METHODS: An online survey was sent out to donor surgeons registered with the Canadian Society of Transplantation. Surveys were anonymous and voluntary. Descriptive statistics were used to analyze the collected responses. Recollection of the sequelae and outcomes from device malfunction were also queried. RESULTS: Twenty-eight of 37 surgeons (76% response rate) responded to the survey. At least one surgeon from every institution in Canada performing LDN responded to the survey. Laparoscopic stapler is the most commonly used device for securing the renal artery (61%) and renal vein (67%). Overall, surgeons felt the stapler was the safest method of securing the renal artery. Stapler misfire and clip slippage were reported by eight (28.5%) and 12 (43%) surgeons, respectively. Most cases were salvageable: laparoscopically (30%), open conversion (30%), and by hand port (5%). Slippage of a plastic locking clip resulted in one emergent laparotomy on POD#1 and one stapler misfire was converted to open resulting in donor death. CONCLUSIONS: Although rare, hemorrhagic complications can occur from device malfunction resulting in poor outcomes for healthy volunteers undergoing LDN. Surgeons need to remain vigilant when selecting the appropriate modality for vascular control.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".