Evidence map›Paper›PMID 33041213›Full record

ArticleOral oncology2021

Using 3D-printed nose models in nasopharyngeal swab training.

Jeremy Chee, Xinyi Lin, Wei Sian Lim, Woei Shyang Loh, Mark Thong, Lishia Ng

Abstract readLetter
In one paragraph

Article in Oral oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. 3D Bioprinting in Otolaryngology: A Review.Advanced healthcare materials · 2023
    Review
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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

6 authors.

Jeremy CheeDepartment of Otolaryngology-Head and Neck Surgery, National University Hospital, Singapore. Electronic address: Jeremy_chee@nuhs.edu.sg.
Xinyi LinDepartment of Otolaryngology-Head and Neck Surgery, National University Hospital, Singapore.
Wei Sian LimDepartment of Otolaryngology-Head and Neck Surgery, National University Hospital, Singapore.
Woei Shyang LohDepartment of Otolaryngology-Head and Neck Surgery, National University Hospital, Singapore.
Mark ThongDepartment of Otolaryngology-Head and Neck Surgery, National University Hospital, Singapore.
Lishia NgDepartment of Otolaryngology-Head and Neck Surgery, National University Hospital, Singapore.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe COVID-19 pandemic has swept across the globe with massive effects on health care systems as well as global economies. Enhanced testing has been put forward as a means to reduce transmission while awaiting the development of targeted therapy or effective vaccination. However, achieving accurate testing necessitates proper nasopharyngeal swab techniques. METHODS AND

resultsWe aimed to design and investigate the utility of an anatomically accurate three-dimensional (3D) printed model of the nose in the training for nasopharyngeal swabs. These models were implemented during training sessions for healthcare workers. All participants surveyed felt that the 3D printed models were useful and beneficial in the training of nasopharyngeal swab techniques.

conclusions3D printed nose models are a useful tool in nasopharyngeal swab training. Their usage may help to facilitate the training of potential swabbing manpower in the upscaling of testing capabilities and volumes in this COVID-19 era.

Indexed as

PandemicsSARS-CoV-2COVID-19COVID-19 TestingDiagnostic Tests, RoutineHealth PersonnelHumansImaging, Three-DimensionalNasopharynxNosePrinting, Three-DimensionalSpecimen Handling3D-printingCOVID-19Nasopharyngeal swabTraining

Identifiers

PMID33041213
PMCPMC7544633

What OpenQuestion holds

Textmetadata
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.