Use of Radiofrequency in Robot-Assisted Partial Nephrectomy for Small Tumors: A Novel Technique
Bibliographic record
Abstract
Introduction and Objectives: Radiofrequency is standardized for ablating small renal tumors, but evidence regarding its effects remains limited. Partial nephrectomy, the gold standard, often leads to hemorrhagic complications and irreversible renal damage due to hilum clamping. To mitigate these risks, we propose a novel technique that replaces clamping with radiofrequency ablation of the tumor for hemostasis in robot-assisted partial nephrectomy. Methods: We report on 357 consecutive patients with T1a renal tumors treated with robot-assisted surgery between 2010 and July 2024. Radiofrequency was used peri-tumorally for hemostasis, followed by complete lesion enucleation. Follow-up included ultrasound and creatinine at 1 month, CT scans at months 3 and 9, and then annually for 5 years. Results: The median age was 60.2 years, with 251 men (70.3%). The median tumor size was 22 mm, and the median blood loss was 15 mL. Hemorrhagic complications occurred in eight patients (2.2%), with one requiring a blood transfusion (0.28%). A total of 30 patients experienced transient stage 1 acute kidney disease (8.4%), with no significant change in median 74.92 mL/min/1.77 m2 vs. 78.77 mL/min/1.77 m2 vs. (p-value 0.15). The median follow-up was 48.2 months, with no tumor recurrence at the treated site. Renal cell carcinoma was found in 83.7% of tumors. Conclusions: To our knowledge, this series represent the largest global undertaking of renal tumor treatment using peripheral radiofrequency ablation without clamping, demonstrating optimal surgical and oncological outcomes, lower morbidity, and fewer complications compared to those noted in the revised literature regarding traditional clamping techniques.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".