Laparoscopic partial nephrectomy: The McMaster University
Bibliographic record
Abstract
Introduction: Laparoscopic partial nephrectomy (LPN) remains one of the more challenging procedures in urology. Minimizing warm ischemia time (WIT) and bleeding requires efficient intracorporeal suturing. In addition, achieving negative surgical margins requires complete excision of the tumour. We report a large Canadian series of laparoscopic partial nephrectomy with intermediate follow-up.Methods: Between September 2000 and August 2008, 152 consecutive laparoscopic partial nephrectomies were performed at our centre. Demographic, pathological and clinical data were collected through a retrospective review of the charts.Results: The average tumour size was 2.68 cm (Range: 0.5-8.8.The vast majority of tumours were malignant (80%). All marginswere negative, except for 2 patients who underwent an immediate re-resection. There were no local recurrences or distant metastasis during the follow-up period of 44.3 months. Most procedures required hilar clamping (93.4%) with a mean WIT of 34 minutes, with a clear trend for declining WIT with increasing experience. Live procedures were converted to laparoscopic radical nephrectomy, 10 converted to a hand-assisted procedure, and 1 was converted to an open partial nephrectomy. The average blood loss was 162 cc. Complications related to the procedure were classified according to the Clavien grading system. The average drop in the glomerular filtration rate was calculated by the Modification of Dietin Renal Disease (MDRD) Study equation between preoperativeand 2.5 months postoperative was 8.6 mL/min/1.73 m2.Conclusions: LPN is a challenging procedure that requires advanced laparoscopic skills. LPN is feasible with excellent oncological outcomes, and an acceptable complication profile. The short-term impact on overall renal function is minimal. The most common postoperative complication was pseudo-aneurysm requiring embolization, which reinforces the intra-operative need for meticulous and a quick suture-ligation of blood vessels during LPN.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.055 | 0.008 |
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".