Comparison of Mortality and Morbidity of Robotic Versus Laparoscopic Radical Nephrectomy for the Treatment of Renal Cell Carcinoma—An Analysis of the National Surgery Quality Improvement Program (NSQIP) Targeted Nephrectomy Database
Bibliographic record
Abstract
Objectives: To compare the perioperative complications between robot-assisted (RARN) and laparoscopic (LRN) radical nephrectomy for the treatment of renal cell carcinoma (RCC). Methods: We conducted a retrospective study using the National Surgical Quality Improvement Program (NSQIP) Nephrectomy-Targeted database from 2019 to 2021. After using propensity score matching, we assessed the association between LRN vs. RARN and the outcomes of interest (primary outcomes of 30-day mortality, return to the operating room, myocardial infarction, and stroke; and secondary outcomes of perioperative complications and nephrectomy-specific outcomes). Results: Among the 1545 patients in the study (mean age: 62.9 ± 11.8 years), 722 underwent RARN and 823 underwent LRN. We did not observe any differences in the major complications between the two approaches. However, LRN was associated with an increased chance of surgical site infections compared with RARN (LRN 2.68% vs. RARN 1.19%, p = 0.047). LRN was also associated with a higher likelihood of a prolonged length of stay (OR 1.54, 95% CI: 1.15, 2.06, p = 0.004) and had a 2.7 times higher chance of conversion rate to open surgery (OR 3.70, 95% CI: 3.25, 4.15, p < 0.001) relative to RARN. However, RARN was associated with a longer operative time than LRN (estimated coefficient 30.67, p < 0.001). Conclusion: We found no significant difference in the major complications between RARN and LRN for patients undergoing radical nephrectomy. At the expense of a somewhat longer operative time, RARN was associated with a lower risk of SSI and a lower conversion rate to open RN. LRN and RARN should both be considered and selected on an individualized basis using tumor, patient, and physician factors.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".