Evidence map›Paper›PMID 41414726›Full record

ArticleJournal of applied clinical medical physics2026

Simulation training in radiation oncology.

Michael C Schell, Andrew W Beavis, Derek W Brown, Michael Milano, Michael O'Neill, Yan Yu, Daniel C Pavord, Peter Dunscombe

Abstract read
In one paragraph

Article in Journal of applied clinical medical physics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Simulation training in radiation oncology.Journal of applied clinical medical physics · 2026
    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

8 authors.

Michael C SchellUniversity of Rochester Medical Center, Rochester, New York, USA.
Andrew W BeavisQueens Centre for Oncology & Haematology, Cottingham, UK.
Derek W BrownUniversity of California, San Diego, La Jolla, California, USA.
Michael MilanoUniversity of Rochester, Rochester, New York, USA.
Michael O'NeillAlan B Pearson Regional Cancer Center, Lynchburg, Virginia, USA.
Yan YuRadiation Oncology, Thomas Jefferson University, Kimmel Cancer Center, Philadelphia, USA.
Daniel C PavordAllegheny General Hospital, Pittsburgh, Pennsylvania, USA.ORCID https://orcid.org/0000-0003-1658-6635
Peter Dunscombe *

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Simulation training can be used in the design of new processes or, more commonly, to train and re-train healthcare personnel in standard processes. Simulation training is one avenue to making radiation oncology a high reliability health care intervention. In this paper, we explore the relevance of simulation training to the provision of high-quality and safe radiation oncology practice against the background of simulation training conducted in high reliability industries. The use of simulation training in healthcare, as well as specific examples in radiation oncology, is reviewed. An outline of the steps for developing a simulation training program is provided. Simulation training is critical for the verification of training and treatment delivery. As such, it increases the integrity and transparency of patient care.

Indexed as

Computer SimulationHealth PersonnelNeoplasmsRadiation OncologySimulation TrainingHumanscompetencysafetysimulation training

Identifiers

PMID41414726
PMCPMC12715271

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.