Evidence map›Paper›PMID 36654967›Full record

Article3D printing and additive manufacturing2022

Defining Soft Tissue: Bitmap Printing of Soft Tissue for Surgical Planning.

Nicholas Jacobson, Erik Carerra, Lawrence Smith, Lorna Browne, Nicholas Stence, Alison Sheridan, Robert MacCurdy

Abstract read
In one paragraph

Article in 3D printing and additive manufacturing, 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. Article
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

7 authors.

Nicholas JacobsonSchool of Engineering, Design and Computation-Inworks Innovation Initiative, University of Colorado: Anschutz Medical Campus, Aurora, Colorado, USA.ORCID https://orcid.org/0000-0001-6634-3338
Erik CarerraSchool of Engineering, Design and Computation-Inworks Innovation Initiative, University of Colorado: Anschutz Medical Campus, Aurora, Colorado, USA.
Lawrence SmithSchool of Engineering, University of Colorado: Boulder, Boulder, Colorado, USA.
Lorna BrownePediatric Radiology, School of Medicine, University of Colorado: Anschutz Medical Campus, Aurora, Colorado, USA.
Nicholas StenceSchool of Medicine, University of Colorado: Anschutz Medical Campus, Aurora, Colorado, USA.
Alison SheridanSchool of Medicine, University of Colorado: Anschutz Medical Campus, Aurora, Colorado, USA.
Robert MacCurdyMechanical Engineering, University of Colorado: Boulder, Boulder, Colorado, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Nearly all applications of 3D printing for surgical planning have been limited to bony structures and simple morphological descriptions of complex organs due to the fundamental limitations in accuracy, quality, and efficiency of the current modeling paradigms and technologies. Current approaches have largely ignored the constitution of soft tissue critical to most surgical specialties where multiple high-resolution variations transition gradually across the interior of the volume. Differences in the scales of organization related to unique organs require special attention to capture fine features critical to surgical procedures. We present a six-material bitmap printing technique for creating 3D models directly from medical images, which are superior in spatial and contrast resolution to current 3D modeling methods, and contain previously unachievable spatial fidelity for soft tissue differentiation. A retrospective exempt IRB was obtained for all data through the Colorado Multiple Institution Review Board #21-3128.

Indexed as

biomedicalbitmapmedical imagingMRImultimaterial

Identifiers

PMID36654967
PMCPMC9809978

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.