Pediatric robotic-assisted surgery is quickly gaining traction in pediatric surgical disciplines but presents unique challenges as compared to adult robotic surgery. Small abdominal and thoracic cavities limit working space and operative indications differ from the adult population. This article describes the development of pediatric robotic-assisted surgery, discusses technical limitations and benefits, and reviews training considerations particular to robotic surgery. Applications and published outcomes of common procedures in urology, general and thoracic surgery, otolaryngology, and pediatric surgical oncology are described. Finally, costs and the anticipated future direction of pediatric robotic-assisted surgery are discussed.
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http://dx.doi.org/10.1016/j.suc.2019.12.004 | DOI Listing |
Surg Endosc
December 2024
Department of Mechanical Engineering and Applied Mechanics, University of Pennsylvania, Philadelphia, USA.
Background: New surgeons experience heavy workload during robot-assisted surgery partially because they must use vision to compensate for the lack of haptic feedback. We hypothesize that providing realistic haptic feedback during dry-lab simulation training may accelerate learning and reduce workload during subsequent surgery on patients.
Methods: We conducted a single-blinded study with 12 general surgery residents (third and seventh post-graduate year, PGY) randomized into haptic and control groups.
Anticancer Res
January 2025
Department of Urology, Hamamatsu University School of Medicine, Hamamatsu, Japan.
Background/aim: Immuno-oncology (IO) improves the prognosis of advanced renal cell carcinoma (RCC). Since research has so far been limited to clinical trials, we herein focused on the effects of IO-tyrosine kinase inhibitor (TKI) combination therapy in real-world clinical settings.
Patients And Methods: We conducted a retrospective study on 125 patients with advanced RCC who received IO-TKI combination therapy or TKI monotherapy.
Bone Jt Open
January 2025
Department of Orthopaedic Surgery, Ehime University Graduate School of Medicine, Toon, Japan.
Aims: Excellent outcomes have been reported following CT-based robotic arm-assisted total hip arthroplasty (rTHA) compared with manual THA; however, its superiority over CT-based navigation THA (nTHA) remains unclear. This study aimed to determine whether a CT-based robotic arm-assisted system helps surgeons perform accurate cup placement, minimizes leg length, and offsets discrepancies more than a CT-based navigation system.
Methods: We studied 60 hips from 54 patients who underwent rTHA between April 2021 and August 2023, and 45 hips from 44 patients who underwent nTHA between January 2020 and March 2021 with the same target cup orientation at the Department of Orthopedic Surgery at Ozu Memorial Hospital, Japan.
Bone Joint J
January 2025
Department of Trauma and Orthopaedics, Glasgow Royal Infirmary, Glasgow, UK.
Aims: The aim of this study was to perform an incremental cost-utility analysis and assess the impact of differential costs and case volume on the cost-effectiveness of robotic arm-assisted medial unicompartmental knee arthroplasty (rUKA) compared to manual (mUKA).
Methods: Ten-year follow-up of patients who were randomized to rUKA (n = 64) or mUKA (n = 65) was performed. Patients completed the EuroQol five-dimension health questionnaire preoperatively, at three months, and one, two, five, and ten years postoperatively, which was used to calculate quality-adjusted life years (QALY) gained and the incremental cost-effectiveness ratio (ICER).
Biom J
February 2025
Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire, USA.
Despite the extensive use of network autocorrelation models in social network analysis, network autocorrelation models for binary dependent variables have received surprisingly scant attention. In this paper, we develop four network autocorrelation models for a binary random variable defined by whether the peer effect (also termed social influence or contagion) acts on latent continuous outcomes leading to an indirect effect under a normal or a logistic distribution or on the probability of the observed outcome itself under a probit or a logit link function defining a direct effect to account for interdependence between outcomes. For all models, we use a Bayesian approach for model estimation under a uniform prior on a transformed peer effect parameter ( ) designed to enhance model computation and compare results to those under the uniform prior for .
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