Evidence map›Paper›PMID 38427102›Full record

ArticleJournal of robotic surgery2024

Design and utilisation of a novel, high-fidelity, low-cost, hybrid-tissue simulation model to facilitate training in robot-assisted partial nephrectomy.

Stefanie M Croghan, Miroslav Voborsky, Adam F Roche, Claire Condron, Dara A O'Keeffe, Barry B McGuire

Open access · hybridAbstract read
In one paragraph

Article in Journal of robotic surgery, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
2.3field-weighted citation impact, top 13% 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

1 citing paper in PubMed, 4 citations in OpenAlex.

  1. Article
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

6 authors at 1 institution in 1 country.

Stefanie M CroghanDepartment of Urology and Transplantation, Beaumont Hospital, Dublin, Ireland. stefaniecroghan@rcsi.com.ORCID http://orcid.org/0000-0003-4524-3682
Miroslav VoborskyRCSI SIM Centre for Simulation Education and Research, RCSI University of Medicine and Health Sciences, Dublin, Ireland.
Adam F RocheRCSI SIM Centre for Simulation Education and Research, RCSI University of Medicine and Health Sciences, Dublin, Ireland.
Claire CondronRCSI SIM Centre for Simulation Education and Research, RCSI University of Medicine and Health Sciences, Dublin, Ireland.
Dara A O'KeeffeRCSI SIM Centre for Simulation Education and Research, RCSI University of Medicine and Health Sciences, Dublin, Ireland.
Barry B McGuireRCSI SIM Centre for Simulation Education and Research, RCSI University of Medicine and Health Sciences, Dublin, Ireland.
Royal College of Surgeons in Ireland · IE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Robot-assisted partial nephrectomy (RAPN) has rapidly evolved as the standard of care for appropriately selected renal tumours, offering key patient benefits over radical nephrectomy or open surgical approaches. Accordingly, RAPN is a key competency that urology trainees wishing to treat kidney cancer must master. Training in robotic surgery is subject to numerous challenges, and simulation has been established as valuable step in the robotic learning curve. However, simulation models are often both expensive and suboptimal in fidelity. This means that the number of practice repetitions for a trainee may limited by cost restraints, and that trainees may struggle to reconcile the skills obtained in the simulation laboratory with real-world practice in the operating room. We have developed a high-fidelity, low-cost, customizable model for RAPN simulation based on porcine tissue. The model has been utilised in teaching courses at our institution, confirming both feasibility of use and high user acceptability. We share the design of our model in this proof-of-concept report.

Indexed as

Carcinoma, Renal CellKidney NeoplasmsRoboticsRobotic Surgical ProceduresAnimalsHumansNephrectomySwineTreatment OutcomeDesignModelPartial nephrectomyRobotic surgerySimulationTraining

Identifiers

PMID38427102
PMCPMC10907476
OpenAlexW4392357138

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.