Evidence map›Paper›PMID 31579786›Full record

ReviewInnovative surgical sciences2018

Artificial vascular models for endovascular training (3D printing).

Inez Torres, Nelson De Luccia

Abstract readReview
In one paragraph

Review in Innovative surgical sciences, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

15 citing papers in PubMed.

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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

2 authors.

Inez TorresDiscipline of Vascular and Endovascular Surgery, Department of Surgery, São Paulo University Medical School, Rua Oscar Freire, 1546, ap 33, Pinheiros, São Paulo - SP 05409-010, Brazil.
Nelson De LucciaDiscipline of Vascular and Endovascular Surgery, Department of Surgery, São Paulo University Medical School, São Paulo, Brazil.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The endovascular technique has led to a revolution in the care of patients with vascular disease; however, acquiring and maintaining proficiency over a broad spectrum of procedures is challenging. Three-dimensional (3D) printing technology allows the production of models that can be used for endovascular training. This article aims to explain the process and technologies available to produce vascular models for endovascular training, using 3D printing technology. The data are based on the group experience and a review of the literature. Different 3D printing methods are compared, describing their advantages, disadvantages and potential roles in surgical training. The process of 3D printing a vascular model based on an imaging examination consists of the following steps: image acquisition, image post-processing, 3D printing and printed model post-processing. The entire process can take a week. Prospective studies have shown that 3D printing can improve surgical planning, especially in complex endovascular procedures, and allows the production of efficient simulators for endovascular training, improving residents' surgical performance and self-confidence.

Indexed as

3D printingendovascularpatient-specificsimulationstraining

Identifiers

PMID31579786
PMCPMC6604582

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.