Comparison of Outcomes Between Partial and Radical Laparoscopic Nephrectomy for Localized Renal Tumors Larger Than Four Centimeters: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Earlier studies have juxtaposed different laparoscopic methods for treating renal tumors; however, extensive evidence with a particular focus on large kidney tumors remains lacking. The objective of this meta-analysis was to assess the perioperative outcomes, kidney performance, and cancer-related results of laparoscopic partial nephrectomy (LPN) versus laparoscopic radical nephrectomy (LRN) for treating extensive, localized, non-metastatic kidney tumors (cT1b-cT2N0M0). Methods: We systematically searched multiple databases from database inception until December 2023 for relevant studies. Selected data were analyzed with the Cochrane Collaboration's Review Manager 5.4 software using a random-effects model. Outcomes were expressed as odds ratios and weighted mean differences with 95% confidence intervals, considering a P value of < 0.05 as significant. Results: Data from nine studies encompassing 1,303 patients (529 LPN, 774 LRN) revealed that LPN was associated with lengthier surgeries and increased blood loss compared to LRN. While LPN exhibited higher postoperative complication rates, the disparity did not reach statistical significance. LPN led to improved postoperative renal function, manifesting as a reduced estimated glomerular filtration rate (eGFR) decline and fewer incidents of new chronic kidney disease cases. Both groups demonstrated comparable tumor recurrence and overall mortality rates, but LPN exhibited significantly lower cancer-specific mortality rates. Conclusions: LPN, despite longer operative times and greater intraoperative blood loss, was found to be superior to LRN in preserving postoperative renal function. Oncologically, LPN and LRN have comparable overall mortality rates, but LPN showed a significant advantage in terms of lower cancer-specific mortality rates.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.038 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".