Evidence map›Paper›PMID 33652844›Full record

ReviewJournal of clinical medicine2021

Application of 3D Printing in Preoperative Planning.

Nicole Segaran, Gia Saini, Joseph L Mayer, Sailen Naidu, Indravadan Patel, Sadeer Alzubaidi, Rahmi Oklu

Abstract readReview
In one paragraph

Review in Journal of clinical medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed, 1 pooled it
–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

28 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Does using 3D printed models for pre-operative planning improve surgical outcomes of foot and ankle fracture fixation? A systematic review and meta-analysis.European journal of trauma and emergency surgery : official publication of the European Trauma Society · 2024
    Pooled it
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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

7 authors.

Nicole SegaranDepartment of Vascular and Interventional Radiology, Minimally Invasive Therapeutics Laboratory, Mayo Clinic, Phoenix, AZ 85054, USA.
Gia SainiDepartment of Vascular and Interventional Radiology, Minimally Invasive Therapeutics Laboratory, Mayo Clinic, Phoenix, AZ 85054, USA.
Joseph L Mayer3D Innovations Laboratory, Mayo Clinic Arizona, 5711 E. Mayo Blvd. Support Services Building, Phoenix, AZ 85054, USA.
Sailen NaiduDepartment of Radiology, Mayo Clinic, Phoenix, AZ 85054, USA.
Indravadan PatelDepartment of Radiology, Mayo Clinic, Phoenix, AZ 85054, USA.
Sadeer AlzubaidiDepartment of Radiology, Mayo Clinic, Phoenix, AZ 85054, USA.
Rahmi OkluDepartment of Vascular and Interventional Radiology, Minimally Invasive Therapeutics Laboratory, Mayo Clinic, Phoenix, AZ 85054, USA.ORCID 0000-0003-4984-1778

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Preoperative planning is critical for success in the surgical suite. Current techniques for surgical planning are limited; clinicians often rely on prior experience and medical imaging to guide the decision-making process. Furthermore, two-dimensional (2D) presentations of anatomical structures may not accurately portray their three-dimensional (3D) complexity, often leaving physicians ill-equipped for the procedure. Although 3D postprocessed images are an improvement on traditional 2D image sets, they are often inadequate for surgical simulation. Medical 3D printing is a rapidly expanding field and could provide an innovative solution to current constraints of preoperative planning. As 3D printing becomes more prevalent in medical settings, it is important that clinicians develop an understanding of the technologies, as well as its uses. Here, we review the fundamentals of 3D printing and key aspects of its workflow. The many applications of 3D printing for preoperative planning are discussed, along with their challenges.

Indexed as

3D printinganatomical modelsmedical imagingpreoperative planningtechnology

Identifiers

PMID33652844
PMCPMC7956651

What OpenQuestion holds

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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.