Laparoscopic partial nephrectomy for >4 cm renal masses
Bibliographic record
Abstract
INTRODUCTION: Laparoscopic partial nephrectomy (LPN) is frequently used to manage cT1a renal masses. While data on safety and long-term oncological outcomes of LPN for T1a tumours are widely available, it is limited for >T1a lesions. We report our experience with LPN for >4 cm renal masses from a Canadian tertiary centre. METHODS: Between January 2003 and July 2011, 52 consecutive LPN for >4 cm renal masses were performed. Demographic, pathological and clinical data were obtained from a prospectively maintained database. RESULTS: The mean patient age was 60 years (62% male). Median tumour size was 4.8 (range: 4.2-11) cm. The median surgical time was 145 minutes, and the median estimated blood loss was 100 mL. The median warm ischemia time was 24 minutes. Four (7.7%) cases required conversion to open surgery. One case was converted to total nephrectomy for clinical and pathological evidence of T3 disease. The surgical margin was positive in 1 case (1.9%). Four (7.7%) patients developed a urine leak postoperatively; 3 of them managed with a ureteric stent. Four (7.7%) patients developed postoperative bleeding requiring selective angioembolization. The median hospital stay was 4 days. There was no statistically significant difference between preoperative and postoperative estimated glomerular filtration rate and mean arterial blood pressure (p = 0.5, p = 0.1, respectively). CONCLUSION: This series demonstrates that LPN although technically challenging has acceptable short-term surgical outcomes. Long-term assessment of oncological outcomes is required. Laparoscopic partial nephrectomy >4 cm renal tumours should not be considered a standard of care, but excellent results can be achieved in well-selected patients and in experienced hands with no impact in renal function or blood pressure.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".