Evidence map›Paper›PMID 32415369›Full record

ArticleSurgical endoscopy2021

A new platform for laparoscopic training: initial evaluation of the ex-vivo live multivisceral training device.

Xiaobo Wang, Kunsong Zhang, Wenjie Hu, Ming Kuang, Serene Teo, Zhiyong Guo, Qiang Zhao, Xiaoshun He

Abstract read
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In one paragraph

Article in Surgical endoscopy, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 1 pooled it
–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

8 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. [Research progresses in ex vivo liver perfusion technology].Zhonghua gan zang bing za zhi = Zhonghua ganzangbing zazhi = Chinese journal of hepatology · 2025
    Review
  5. Organ medicine: New concept of life sciences.Chinese medical journal · 2025
    Article
  6. Article
  7. Article
  8. Cost-Efficient Video Synthesis and Evaluation for Development of Virtual 3D Endoscopy.IEEE journal of translational engineering in health and medicine · 2021
    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

8 authors.

Xiaobo Wang *Organ Transplant Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, People's Republic of China.
Kunsong Zhang *Department of Hepatobiliary Surgery, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, People's Republic of China.
Wenjie Hu *Department of Hepatobiliary Surgery, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, People's Republic of China.
Ming KuangDepartment of Hepatobiliary Surgery, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, People's Republic of China.
Serene TeoFaculty of Medicine, University of Glasgow, Glasgow, Scotland, UK.
Zhiyong GuoOrgan Transplant Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, People's Republic of China.
Qiang ZhaoOrgan Transplant Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, People's Republic of China. 522511042@qq.com.
Xiaoshun HeOrgan Transplant Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, People's Republic of China. gdtrc@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundVarious training models have been developed for laparoscopic training. Inanimate models including cadavers, ex-vivo simulator, and virtual reality (VR), are less realistic and often fail to display specific events such as bleeding, bile leakage, etc. Animal models provide more realistic experience, but constraints like cost involved, anesthetic requirement, and ethical approval have limited its application. We have designed a new training ex-vivo simulator-Smagister to address these issues.

methodsThe Smagister consists of a normothermic machine perfusion platform, multivisceral organ of porcine abdominal cavity (liver, gallbladder, pancreas, stomach, intestine, kidney, uterus, bladders, etc.), high-definition display, and software system. Blood gas analysis and number of peristalsis per hour were recorded. A questionnaire was used to subjectively assess vitality of the organ cluster every hour. Three laparoscopic procedures including cholecystectomy (LC), enterotomy closure (LEC) and hepatectomy (LLR) were performed on Smagister, with demonstration of specific events for each procedure. Six experts compared the procedures with actual surgery in terms of feasibility to complete procedures and demonstration of complications.

resultsThe fluctuation of perfusate glucose (6.1-8.2 mmol/L) and lactate (5.82-6.55 mmol/L) suggested metabolic function of the multivisceral organs. The mean number of peristalsis was 2.2/min. The simulated surgical view and anatomic structures closely resembled actual surgery during continuous perfusion (3.5 ± 1.0, 3.8 ± 0.8, respectively). The evaluation scores of haptic feedbacks were 3.8 ± 0.8, resembling live tissue handling. LC, LEC, and LLR were performed well on the Smagister, with clear display of the specific events. All six experts considered Smagister as a suitable training modality for both basic and advanced laparoscopic surgery.

conclusionThe amalgamation of live animal model and ex-vivo simulation in Smagister centralizes the virtue of both modalities, expands the training field, and provides high-fidelity laparoscopic training for both novice and senior surgeons.

Indexed as

AnimalsComputer SimulationFemaleHumansLaparoscopyModels, AnimalSwineLaparoscopic trainingLiveMultivisceral organsSimulatorSpecific events

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Registered trials

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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.