Evidence map›Paper›PMID 39499359›Full record

ArticleJournal of robotic surgery2024

A novel low-cost high-fidelity porcine model of liver metastases for simulation training in robotic parenchyma-preserving liver resection.

R M O'Connell, S Horne, D A O'Keeffe, N Murphy, M Voborsky, C Condron, C A Fleming, J B Conneely, B B McGuire

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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 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
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

4 citing papers in PubMed.

  1. Article
  2. Pre-Procedural Robotic Surgery Curricula: A Systematic Review and Thematic Meta-Synthesis.Annals of surgery open : perspectives of surgical history, education, and clinical approaches · 2026
    Review
  3. Review
  4. Article
4 · The record

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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

9 authors.

R M O'ConnellDepartment of Hepatopancreaticobiliary Surgery, Mater Misericordiae University Hospital, Dublin, Ireland. robertmoconnell@rcsi.com.
S HorneRCSI SIM Centre for Simulation Education and Research, RCSI University of Medicine and Health Sciences, Dublin, Ireland.
D A O'KeeffeDepartment of Surgical Affairs, RCSI University of Medicine and Health Sciences, Dublin, Ireland.
N MurphyRCSI SIM Centre for Simulation Education and Research, RCSI University of Medicine and Health Sciences, Dublin, Ireland.
M VoborskyRCSI SIM Centre for Simulation Education and Research, RCSI University of Medicine and Health Sciences, Dublin, Ireland.
C CondronRCSI SIM Centre for Simulation Education and Research, RCSI University of Medicine and Health Sciences, Dublin, Ireland.
C A FlemingRCSI SIM Centre for Simulation Education and Research, RCSI University of Medicine and Health Sciences, Dublin, Ireland.
J B ConneelyDepartment of Hepatopancreaticobiliary Surgery, Mater Misericordiae University Hospital, Dublin, Ireland.
B B McGuireDepartment of Surgical Affairs, RCSI University of Medicine and Health Sciences, Dublin, Ireland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the era of minimally invasive surgery (MIS), parenchyma-preserving liver resections are gaining prominence with the potential to offer improved perioperative outcomes without compromising oncological safety. The surgeon learning curve remains challenging, and simulation plays a key role in surgical training. Existing simulation models can be limited by suboptimal fidelity and high cost. We describe a novel, reproducible, high-fidelity, low-cost liver metastases model using porcine livers from adult Landrace pigs, with porcine perinephric fat used to simulate subcapsular metastases. This model was then utilised in a training session for surgical trainees performing robotic parenchyma-preserving surgery (PPS) under the guidance of expert robotic surgeons, with feedback being recorded. Trainees rated the model highly on its fidelity to human liver simulation (median score 9), tissue handling (median score 8), and overall usefulness (median score 9). Tissue handling was felt to simulate in vivo liver resection closely, while suggestions for improvement included adding simulated blood flow. This is a novel, low-cost, high-fidelity simulation model of liver metastases with high acceptability to surgical trainees, which could be readily adopted by other training centres.

Indexed as

HepatectomyLiver NeoplasmsRobotic Surgical ProceduresAnimalsDisease Models, AnimalHumansLiverSimulation TrainingSwineLiver metastasesPorcine modelRobotic liver resectionSimulation training

Identifiers

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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.