Status of robotic surgery in pediatric genitourinary tumors: A systematic review
Bibliographic record
Abstract
Innovative surgical approaches are crucial in pediatric oncology to enhance treatment outcomes and minimize morbidity. Robotic-assisted surgery (RAS) has shown promise in both surgical precision and recovery in pediatric patients. This systematic review aims to address this gap by examining the current role and impact of RAS in managing pediatric genitourinary tumors, focusing on its feasibility, safety, and patient outcomes. This review was registered with PROSPERO (CRD42023464820). We included studies involving pediatric patients undergoing RAS for genitourinary tumors, focusing on outcomes like conversion rates, resection completeness, and complications. Studies were identified through searches in PubMed, EMBASE, and Scopus until October 2023. Study quality and bias were assessed using ROBINS-I for cohort studies and Joanna Briggs Institute tools for case reports and series. Of 2119 citations, 42 studies were included, comprising 29 case reports, five case series, and eight retrospective cohort studies. Robotic-assisted renal surgeries were most common, with favorable outcomes in terms of resection completeness and low recurrence rates. Adrenal, bladder, and retroperitoneal surgeries also showed promising results, although rare instances required conversion to open surgery. Collaborative efforts and perioperative aids like intraoperative ultrasound and three-dimensional modeling were crucial for success. This work is limited by the lack of large cohort studies and addressing the learning curve associated with these procedures. RAS shows promise in treating pediatric genitourinary tumors, offering precise resections and favorable outcomes, warranting further research and refinement.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".