Evidence map›Paper›PMID 35057409›Full record

ArticleMaterials (Basel, Switzerland)2022

3D Printing of Customizable Phantoms to Replace Cadaveric Models in Upper Extremity Surgical Residency Training.

Elisha Raeker-Jordan, Miguel Martinez, Kenji Shimada

Abstract readCase Reports
In one paragraph

Article in Materials (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. 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

3 authors.

Elisha Raeker-JordanDepartment of Mechanical Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA.ORCID 0000-0003-1769-1373
Miguel MartinezDepartment of Mechanical Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA.ORCID 0000-0002-6132-2631
Kenji ShimadaDepartment of Mechanical Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Medical phantoms are commonly used for training and skill demonstration of surgical procedures without exposing a patient to unnecessary risk. The discrimination of these tissues is critical to the ability of young orthopedic surgical trainees to identify patient injuries and properly manipulate surrounding tissues into healing-compliant positions. Most commercial phantoms lack anatomical specificity and use materials that inadequately attempt to mimic human tissue characteristics. This paper covers the manufacturing methods used to create novel, higher fidelity surgical training phantoms. We utilize medical scans and 3D printing techniques to create upper extremity phantoms that replicate both osseous and synovial geometries. These phantoms are undergoing validation through OSATS training of surgical residents under the guidance of attendings and chief residents. Twenty upper extremity phantoms with distal radius fracture were placed into traction and reduced by first- and second-year surgical residency students as part of their upper extremity triage training. Trainees reported uniform support for the training, enjoying the active learning exercise and expressing willingness for participation in future trials. Trainees successfully completed the reduction procedure utilizing tactile stimuli and prior lecture knowledge, showing the viability of synthetic phantoms to be used in lieu of traditional cadaveric models.

Indexed as

3D printingpersonalized medicinephantomresidency trainingupper extremity

Identifiers

PMID35057409
PMCPMC8779716

What OpenQuestion holds

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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.