Perioperative, functional, and oncological outcomes of robotic vs. laparoscopic partial nephrectomy for complex renal tumors (RENAL score ≥7): an evidence-based analysis
Bibliographic record
Abstract
Objective: To evaluate the current literature comparing outcomes of robotic partial nephrectomy (RPN) versus laparoscopic partial nephrectomy (LPN) treating complex renal tumors (RENAL nephrometry score ≥7). Methods: We systematically searched the Cochrane Library, PubMed, Google Scholar, EMBASE, and Scopus databases up to March 2023. Review Manager 5.4 performed a pooled analysis of the data for random effects. Besides, sensitivity and subgroup analyses to explore heterogeneity, Newcastle-Ottawa scale, and GRADE to evaluate study quality and level of evidence. Results: Eight observational studies comprising 1346 patients (RPN: 695; LPN: 651) were included in this study. Compared to LPN, RPN had a shorter operative time (OT) (weight mean difference [WMD]: -14.73 min; p = 0.0003), shorter warm ischemia time (WIT) (WMD: -3.47 min; p = 0.002), lower transfusion rate (odds ratio [OR]: 0.66; p = 0.04), shorter length of stay (LOS) (WMD: -0.65 days; p < 0.00001), lower postoperative estimated glomerular filtration rate (eGFR) change (WMD = -2.33 mL/min/1.73 m2; p = 0.002) and lower intraoperative complications (OR: 0.52; p = 0.04). No significant differences were observed between the two groups in terms of estimated blood loss (EBL) (p = 0.84), conversion to radical nephrectomy (p = 0.12), postoperative complications (p = 0.11), major complications (defined Clavien-Dindo grade 3 (p = 0.43), overall complications (p = 0.15), postoperative eGFR (p = 0.28), local recurrence (p = 0.35), positive surgical margin (PSM) (p = 0.63), overall survival (OS) (p = 0.47), cancer-specific survival (CSS) (p = 0.22) and 3-year recurrence-free survival (RFS) (p = 0.53). Conclusion: Patients with complex renal tumors (RENAL score ≥7), RPN is superior to LPN in decreasing the OT, WIT, LOS, transfusion rate, change in eGFR and the incidence of intraoperative complications while maintaining oncological control and avoiding a decline in renal function. However, our findings need further validation in a large-sample prospective randomized study.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".