Evidence map›Paper›PMID 37304228›Full record

ArticleEuropean urology open science2023

The First Entirely 3D-Printed Training Model for Robot-assisted Kidney Transplantation: The RAKT Box.

Riccardo Campi, Alessio Pecoraro, Graziano Vignolini, Pietro Spatafora, Arcangelo Sebastianelli, Francesco Sessa, Vincenzo Li Marzi, Angelo Territo, Karel Decaestecker, Alberto Breda and 3 more

Open access · goldAbstract read
In one paragraph

Article in European urology open science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
4.8field-weighted citation impact, top 5% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed, 15 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
  4. Review
  5. Robotic kidney transplantation.Nature reviews. Urology · 2024
    Review
  6. Article
  7. Review
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  9. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors at 3 institutions in 4 countries.

Riccardo CampiUnit of Urological Robotic Surgery and Renal Transplantation, University of Florence, Careggi Hospital, Florence, Italy.
Alessio PecoraroUnit of Urological Robotic Surgery and Renal Transplantation, University of Florence, Careggi Hospital, Florence, Italy.
Graziano VignoliniUnit of Urological Robotic Surgery and Renal Transplantation, University of Florence, Careggi Hospital, Florence, Italy.
Pietro SpataforaUnit of Urological Robotic Surgery and Renal Transplantation, University of Florence, Careggi Hospital, Florence, Italy.
Arcangelo SebastianelliUnit of Urological Robotic Surgery and Renal Transplantation, University of Florence, Careggi Hospital, Florence, Italy.
Francesco SessaUnit of Urological Robotic Surgery and Renal Transplantation, University of Florence, Careggi Hospital, Florence, Italy.
Vincenzo Li MarziUnit of Urological Robotic Surgery and Renal Transplantation, University of Florence, Careggi Hospital, Florence, Italy.
Angelo TerritoEuropean Association of Urology Young Academic Urologists Kidney Transplantation Working Group, Arnhem, The Netherlands.
Karel DecaesteckerEuropean Association of Urology Robotic Urology Section Robot-assisted Kidney Transplantation Working Group, Arnhem, The Netherlands.
Alberto BredaDepartment of Urology, Fundaciò Puigvert, Autonomous University of Barcelona, Barcelona, Spain.
Sergio SerniUnit of Urological Robotic Surgery and Renal Transplantation, University of Florence, Careggi Hospital, Florence, Italy.
RAKT Box Collaborators
European Association of Urology EAU Young Academic Urologists Kidney Transplantation working group the EAU Robotic Urology Section Robot-assisted Kidney Transplantation Working Group
University of Florence · ITEuropean Association of Urology · NLUniversitat Autònoma de Barcelona · ES

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Robot-assisted kidney transplantation (RAKT) is increasingly performed at selected referral institutions worldwide. However, simulation and proficiency-based progression training frameworks for RAKT are still lacking, making acquisition of the RAKT-specific skill set a critical unmet need for future RAKT surgeons. Objective: To develop and test the RAKT Box, the first entirely 3D-printed, perfused, hyperaccuracy simulator for vascular anastomoses during RAKT. Design setting and participants: The project was developed in a stepwise fashion by a multidisciplinary team including urologists and bioengineers via an iterative process over a 3-yr period (November 2019-November 2022) using an established methodology. The essential and time-sensitive steps of RAKT were selected by a team of RAKT experts and simulated using the RAKT Box according to the principles of the Vattituki-Medanta technique. The RAKT Box was tested in the operating theatre by an expert RAKT surgeon and independently by four trainees with heterogeneous expertise in robotic surgery and kidney transplantation. Surgical procedure: Simulation of RAKT. Measurements: Video recordings of the trainees' performance of vascular anastomoses using the RAKT Box were evaluated blind by a senior surgeon according to the Global Evaluative Assessment of Robotic Skills (GEARS) and Assessment of Robotic Console Skills (ARCS) tools. Results and limitations: All participants successfully completed the training session, confirming the technical reliability of the RAKT Box simulator. Tangible differences were observed among the trainees in both anastomosis time and performance metrics. Key limitations of the RAKT Box include lack of simulation of the ureterovesical anastomosis and the need for a robotic platform, specific training instruments, and disposable 3D-printed vessels. Conclusions: The RAKT Box is a reliable educational tool to train novice surgeons in the key steps of RAKT and may represent the first step toward the definition of a structured surgical curriculum in RAKT. Patient summary: We describe the first entirely 3D-printed simulator that allows surgeons to test the key steps of robot-assisted kidney transplantation (RAKT) in a training environment before performing the procedure in patients. The simulator, called the RAKT Box, has been successfully tested by an expert surgeon and four trainees. The results confirm its reliability and potential as an educational tool for training of future RAKT surgeons.

Indexed as

Renal transplantationRoboticsSimulationThree-dimensional printingTraining

Identifiers

PMID37304228
PMCPMC10251129
OpenAlexW4379135741

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.