Perioperative outcomes for laparoscopic radical nephrectomies performed on ≥ 10 cm tumors.
Bibliographic record
Abstract
INTRODUCTION: The role of laparoscopic radical nephrectomy (LRN) in the management of very large renal masses has yet to be determined. Moreover, no studies have considered the total size of the specimen removed. We report our experience managing renal masses ≥ 10 cm with transperitoneal LRN. MATERIALS AND METHODS: We retrospectively reviewed cases of LRN performed in the context of renal masses from 2006 to 2012 at our institution. LRNs were divided into two groups; tumors 10 cm or larger (n = 24) and tumors smaller than 10 cm (n = 124). Patient demographics, tumor characteristics, operative and perioperative outcomes were compared. Complication rate was assessed in relation to tumor and specimen size. RESULTS: Mean pathologic tumor size was 11.8 cm (range 10.0 cm-17.0 cm) and 5.8 cm (range 2.1 cm-9.5 cm) for tumors ≥ 10 cm and < 10 cm, respectively. No difference was found in demographic characteristics, operative and perioperative outcomes (estimated blood loss, rate of conversion to open radical nephrectomy, length of postoperative stay and complication rate), between both groups, except higher surgical time in the ≥ 10 cm group (171 min versus 143 min, respectively, p = 0.005). There was no difference in tumor and total specimen size between patients with and without complications. Due to its retrospective nature, the major limitation of this study is missing data regarding specimen size. CONCLUSION: LRN can be performed safely with acceptable operative and perioperative outcomes by experienced laparoscopists for very large renal masses (≥ 10 cm). Complication rates were unrelated to tumor and total specimen size.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".