Robotic versus Laparoscopic Partial Nephrectomy in the New Era: Systematic Review
Bibliographic record
Abstract
(1) Background: In recent years there have been advances in imaging techniques, in addition to progress in the surgery of renal tumors directed towards minimally invasive techniques. Thus, nephron-sparing surgery has become the gold standard for the treatment of T1 renal masses. The aim of this study is to investigate the benefits of robotic partial nephrectomy in comparison with laparoscopic nephrectomy. (2) Methods: We performed a systematic review according to the PRISMA criteria during September 2022. We included clinical trials, and cohort and case-control studies published between 2000 and 2022. This comprised studies performed in adult patients with T1 renal cancer and studies comparing robotic with open and laparoscopic partial nephrectomy. A risk of bias assessment was performed according to the Newcastle-Ottawa scale. (3) Results: We observed lower hot ischemia times in the robotic surgery groups, although at the cost of an increase in total operative time, without appreciating the differences in terms of serious surgical complications (Clavien III-V). (4) Conclusions: Robotic partial nephrectomy is a safe procedure, with a shorter learning curve than laparoscopic surgery and with all the benefits of minimally invasive surgery.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".