Evidence map›Paper›PMID 38391683›Full record

ArticleBioengineering (Basel, Switzerland)2024

Surgical Medical Education via 3D Bioprinting: Modular System for Endovascular Training.

Ruben Foresti, Anna Fornasari, Claudio Bianchini Massoni, Arianna Mersanne, Chiara Martini, Elisa Cabrini, Antonio Freyrie, Paolo Perini

Abstract read
In one paragraph

Article in Bioengineering (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Article
  3. An intuitive guidewire control mechanism for robotic intervention.International journal of computer assisted radiology and surgery · 2025
    Article
  4. Article
  5. Article
  6. 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

8 authors.

Ruben ForestiDepartment of Medicine and Surgery, University of Parma, Via Gramsci 14, 43126 Parma, Italy.ORCID 0000-0002-1060-978X
Anna FornasariVascular Surgery, Cardio-Thoracic and Vascular Department, University-Hospital of Parma, 43126 Parma, Italy.ORCID 0000-0001-9297-3296
Claudio Bianchini MassoniVascular Surgery, Cardio-Thoracic and Vascular Department, University-Hospital of Parma, 43126 Parma, Italy.ORCID 0000-0003-1953-8073
Arianna MersanneVascular Surgery, Cardio-Thoracic and Vascular Department, University-Hospital of Parma, 43126 Parma, Italy.
Chiara MartiniDepartment of Medicine and Surgery, University of Parma, Via Gramsci 14, 43126 Parma, Italy.ORCID 0000-0002-0877-4954
Elisa CabriniVascular Surgery, Cardio-Thoracic and Vascular Department, University-Hospital of Parma, 43126 Parma, Italy.
Antonio FreyrieDepartment of Medicine and Surgery, University of Parma, Via Gramsci 14, 43126 Parma, Italy.ORCID 0000-0002-7080-3138
Paolo PeriniDepartment of Medicine and Surgery, University of Parma, Via Gramsci 14, 43126 Parma, Italy.ORCID 0000-0003-4554-416X

Funding

Ministero della Salute GR-2019-12369941
6 · The paper itself

Abstract

There is currently a shift in surgical training from traditional methods to simulation-based approaches, recognizing the necessity of more effective and controlled learning environments. This study introduces a completely new 3D-printed modular system for endovascular surgery training (M-SET), developed to allow various difficulty levels. Its design was based on computed tomography angiographies from real patient data with femoro-popliteal lesions. The study aimed to explore the integration of simulation training via a 3D model into the surgical training curriculum and its effect on their performance. Our preliminary study included 12 volunteer trainees randomized 1:1 into the standard simulation (SS) group (3 stepwise difficulty training sessions) and the random simulation (RS) group (random difficulty of the M-SET). A senior surgeon evaluated and timed the final training session. Feedback reports were assessed through the Student Satisfaction and Self-Confidence in Learning Scale. The SS group completed the training sessions in about half time (23.13 ± 9.2 min vs. 44.6 ± 12.8 min). Trainees expressed high satisfaction with the training program supported by the M-SET. Our 3D-printed modular training model meets the current need for new endovascular training approaches, offering a customizable, accessible, and effective simulation-based educational program with the aim of reducing the time required to reach a high level of practical skills.

Indexed as

3D model3D printingendovascular surgerysurgical educationsurgical simulationvascular surgery

Identifiers

PMID38391683
PMCPMC10886183

What OpenQuestion holds

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LicenceCC BY
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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.