Evidence map›Paper›PMID 37439899›Full record

Article3D printing in medicine2023

Design, printing optimization, and material testing of a 3D-printed nasal osteotomy task trainer.

Lauren Schlegel, Eric Malani, Sara Belko, Ayan Kumar, Eric Barbarite, Howard Krein, Ryan Heffelfinger, Morgan Hutchinson, Robert Pugliese

Erratum issuedOpen access · goldAbstract read
In one paragraph

Article in 3D printing in medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
2.2field-weighted citation impact, top 12% of its field
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

2 citing papers in PubMed, 7 citations in OpenAlex.

  1. Enhancing Facial Filler Training with 3D-Printed Models: A Prospective Observational Study on Medical Student Competence.Medical science monitor : international medical journal of experimental and clinical research · 2024
    Observational
  2. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors at 1 institution in 1 country.

Lauren SchlegelSidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA, USA. lschlege@bidmc.harvard.edu.
Eric MalaniSidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA, USA.
Sara BelkoSidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA, USA.
Ayan KumarDepartment of Otolaryngology, Thomas Jefferson University, Philadelphia, PA, USA.
Eric BarbariteDepartment of Otolaryngology, Thomas Jefferson University, Philadelphia, PA, USA.
Howard KreinDepartment of Otolaryngology, Thomas Jefferson University, Philadelphia, PA, USA.
Ryan HeffelfingerDepartment of Otolaryngology, Thomas Jefferson University, Philadelphia, PA, USA.
Morgan HutchinsonHealth Design Lab, Thomas Jefferson University, Philadelphia, PA, USA.
Robert PuglieseHealth Design Lab, Thomas Jefferson University, Philadelphia, PA, USA.
Thomas Jefferson University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundFor difficult or rare procedures, simulation offers an opportunity to provide education and training. In developing an adequate model to utilize in simulation, 3D printing has emerged as a useful technology to provide detailed, accessible, and high-fidelity models. Nasal osteotomy is an essential step in many rhinoplasty surgeries, yet it can be challenging to perform and difficult to receive adequate exposure to this nuanced portion of the procedure. As it currently stands, there are limited opportunities to practice nasal osteotomy due to the reliance on cadaveric bones, which are expensive, difficult to obtain, and require appropriate facilities and personnel. While previous designs have been developed, these models leave room for improvement in printing efficiency, cost, and material performance. This manuscript aims to describe the methodology for the design of an updated nasal osteotomy training model derived from anatomic data and optimized for printability, usability, and fidelity. Additionally, an analysis of multiple commercially available 3D printing materials and technologies was conducted to determine which offered superior equivalency to bone.

methodsThis model was updated from a first-generation model previously described to include a more usable base and form, reduce irrelevant structures, and optimize geometry for 3D printing, while maintaining the nasal bones with added stabilizers essential for function and fidelity. For the material comparison, this updated model was printed in five materials: Ultimaker Polylactic Acid, 3D Printlife ALGA, 3DXTECH SimuBone, FibreTuff, and FormLabs Durable V2. Facial plastic surgeons tested the models in a blinded, randomized fashion and completed surveys assessing tactile feedback, audio feedback, material limitation, and overall value.

resultsA model optimizing printability while maintaining quality in the area of interest was developed. In the material comparison, SimuBone emerged as the top choice amongst the evaluating physicians in an experience-based subjective comparison to human bone during a simulated osteotomy procedure using the updated model.

conclusionThe updated midface model that was user-centered, low-cost, and printable was designed. In material testing, Simubone was rated above other materials to have a more realistic feel.

Indexed as

3D printingBone materialMaterial comparisonMedical educationSimulationSurgery

Identifiers

PMID37439899
PMCPMC10339601
OpenAlexW4384120203

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.