Evidence map›Paper›PMID 40098829›Full record

ArticleAnnals of thoracic surgery short reports2025

Novel Robotic Esophagogastric Anastomosis Simulation Model for Skill Development and Training.

Lye-Yeng Wong, Devanish Kamtam, Jake Kim, Bailey Wallen, Irmina A Elliott, Brandon A Guenthart, Douglas Z Liou, Leah M Backhus, Mark F Berry, Joseph B Shrager and 1 more

Abstract read
In one paragraph

Article in Annals of thoracic surgery short reports, 2025. 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
–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

1 citing paper in PubMed.

  1. 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

11 authors.

Lye-Yeng WongDivision of Thoracic Surgery, Department of Cardiothoracic Surgery, Stanford University School of Medicine, Stanford, California.
Devanish KamtamDivision of Thoracic Surgery, Department of Cardiothoracic Surgery, Stanford University School of Medicine, Stanford, California.
Jake KimDivision of Thoracic Surgery, Department of Cardiothoracic Surgery, Stanford University School of Medicine, Stanford, California.
Bailey WallenDivision of Thoracic Surgery, Department of Cardiothoracic Surgery, Stanford University School of Medicine, Stanford, California.
Irmina A ElliottDivision of Thoracic Surgery, Department of Cardiothoracic Surgery, Stanford University School of Medicine, Stanford, California.
Brandon A GuenthartDivision of Thoracic Surgery, Department of Cardiothoracic Surgery, Stanford University School of Medicine, Stanford, California.
Douglas Z LiouDivision of Thoracic Surgery, Department of Cardiothoracic Surgery, Stanford University School of Medicine, Stanford, California.
Leah M BackhusDivision of Thoracic Surgery, Department of Cardiothoracic Surgery, Stanford University School of Medicine, Stanford, California.
Mark F BerryDivision of Thoracic Surgery, Department of Cardiothoracic Surgery, Stanford University School of Medicine, Stanford, California.
Joseph B ShragerDivision of Thoracic Surgery, Department of Cardiothoracic Surgery, Stanford University School of Medicine, Stanford, California.
Natalie S LuiDivision of Thoracic Surgery, Department of Cardiothoracic Surgery, Stanford University School of Medicine, Stanford, California.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Esophagogastric anastomosis is a critical step of esophagectomy. We aimed to develop a novel robotic esophagectomy simulator with high rates of fidelity and educational value for trainee surgeons to advance these skills in a low-risk setting. Methods: A porcine esophagus-stomach block was secured on a platform resembling the anatomy during an esophagectomy, and a da Vinci Xi (Intuitive Surgical) robotic system was docked above it. Participants completed 5 key steps (creating the gastric conduit, transecting the esophagus, making the gastrotomy and esophagotomy, creating the anastomosis, and sewing the common enterotomy). The model was assessed through surveys under domains of fidelity (surgical field, reality of materials, anatomy, and experience) and value as a training tool on a scale of 1 to 5 (strongly disagree to strongly agree). Results: Of 14 participants, 8 (57.1%) were women, 9 (64.3%) were integrated cardiothoracic surgery residents, 1 (7.1%) was a thoracic-track resident, and 10 (71.4%) were in postgraduate year 4 or higher. Participants thought most aspects of the model had high fidelity, including the anatomy of conduit (4.8 ± 0.4) and proximal esophagus (4.9 ± 0.4), realism of the stomach (4.9 ± 0.4) and esophagus (4.9 ± 0.4), stapling (4.7 ± 0.6), suturing (4.8 ± 0.4), and tissue handling (4.4 ± 0.6). Participants rated the model highly overall (4.7 ± 0.5) and as a training tool (4.9 ± 0.4), with strong interrater reliability (0.69). Conclusions: The robotic esophagogastric simulation model demonstrated high fidelity and value as a training tool, suggesting its potential effectiveness for surgeons with limited experience. However, it warrants further refinement to address limitations and to optimize its value as a training tool.

Identifiers

PMID40098829
PMCPMC11910767

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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.