Evidence map›Paper›PMID 36164438›Full record

ArticleTechnical innovations & patient support in radiation oncology2022

Virtual reality and augmented reality in radiation oncology education - A review and expert commentary.

David L Kok, Sathana Dushyanthen, Gabrielle Peters, Daniel Sapkaroski, Michelle Barrett, Jenny Sim, Jesper Grau Eriksen

Abstract read
In one paragraph

Article in Technical innovations & patient support in radiation oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

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

  1. Pooled it
  2. Article
  3. Review
  4. Article
  5. Article
  6. Article
  7. Review
  8. Smartphone-based augmented reality patient education in radiation oncology.Technical innovations & patient support in radiation oncology · 2024
    Article
  9. Review
  10. Article
  11. Article
  12. Article
  13. Scholarship in radiation oncology education.Technical innovations & patient support in radiation oncology · 2023
    Article
  14. Screen-based digital learning methods in radiation oncology and medical education.Technical innovations & patient support in radiation oncology · 2022
    Article
  15. 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

7 authors.

David L KokPeter MacCallum Cancer Centre - Moorabbin Campus, 865 Centre Rd, Bentleigh East Victoria 3165, Australia.
Sathana DushyanthenDepartment of Clinical Pathology, University of Melbourne, Parkville Victoria 3010, Australia.
Gabrielle PetersDepartment of Therapeutic Radiology, Yale University, Yale School of Medicine, 333 Cedar St, New Haven, CT 06510, United States.
Daniel SapkaroskiPeter MacCallum Cancer Centre, 305 Grattan St, Melbourne Victoria 3000, Australia.
Michelle BarrettVictorian Comprehensive Cancer Centre, 305 Grattan St, Melbourne Victoria 3000, Australia.
Jenny SimDepartment of Medical Imaging and Radiation Sciences, Monash University, Wellington Rd, Clayton Victoria 3800, Australia.
Jesper Grau EriksenDepartment of Experimental Clinical Oncology, Aarhus University Hospital, Aarhus Municipality, Denmark.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The field of radiation oncology is rapidly advancing through technological and biomedical innovation backed by robust research evidence. However, cancer professionals are notoriously time-poor, meaning there is a need for high quality, accessible and tailored oncologic education programs. While traditional teaching methods including lectures and other in-person delivery formats remain important, digital learning (DL) has provided additional teaching options that can be delivered flexibly and on-demand from anywhere in the world. While evidence of this digital migration has been evident for some time now, it has not always been met with the same enthusiasm by the teaching community, in part due to questions about its pedagogical effectiveness. Many of these reservations have been driven by a rudimentary utilisation of the medium and inexperience with digital best-practice. With increasing familiarity and understanding of the medium, increasingly sophisticated and pedagogically-driven learning solutions can be produced. This article will review the application of immersive digital learning tools in radiation oncology education. This includes first and second-generation Virtual Reality (VR) environments and Augmented Reality (AR). It will explore the data behind, and best-practice application of, each of these tools as well as giving practical tips for educators who are looking to implement (or refine) their use of these learning methods. It includes a discussion of how to match the digital learning methods to the content being taught and ends with a horizon scan of where the digital medium may take us in the future. This article is the second in a two-part series, with the companion piece being on Screen-Based Digital Learning Methods in Radiation Oncology. Overall, the digital space is well-placed to cater to the evolving educational needs of oncology learners. Further uptake over the next decade is likely to be driven by the desire for flexible on demand delivery, high-yield products, engaging delivery methods and programs that are tailored to individual learning needs. Educational programs that embrace these principles will have unique opportunities to thrive in this space.

Indexed as

AR, Augmented RealityAugmented realityCancer educationDigital learningDL, Digital LearningImmersive learningMedical educationOnline learningRadiation OncologyRadiotherapySimulated Learning EnvironmentSLE, Simulated Learning EnvironmentVirtual realityVRVR, Virtual Reality

Identifiers

PMID36164438
PMCPMC9508152

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.