Evidence map›Paper›PMID 32620522›Full record

ArticleJournal of medical imaging and radiation sciences2020

A Survey Exploring Personalised Medicine amongst Radiography Academics within the United Kingdom.

Jerome Atutornu, Christopher M Hayre

Abstract read
In one paragraph

Article in Journal of medical imaging and radiation sciences, 2020. 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. 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

2 authors.

Jerome AtutornuDiagnostic Radiography, School of Health and Sports Science, University of Suffolk, Ipswich, United Kingdom. Electronic address: j.atutornu@uos.ac.uk.
Christopher M HayreDiagnostic Radiography, School of Dentistry and Health Sciences, Charles Sturt University, New South Wales, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionThis article explores the application of personalized medicine (PM) within the academic environment in the United Kingdom. There is a growing acceptance of the utility of PM in health care and the role medical imaging can play. In response, this article explored the views and opinions of diagnostic and therapeutic academics and the utilization of PM in education.

methodsThis study primarily adopted a quantitative approach using an online survey. However, participants were also encouraged to provide qualitative comments in response to open-ended questions. The survey was distributed to radiography and radiotherapy academic departments, which received a response rate of 29%.

resultsThe findings identify some important considerations. On the one hand some participants reported teaching PM on their programmes (24%, n = 16), whereas 30% (n = 20) did not. Importantly, the remaining academics (46%) were either unsure or did not know what PM was. This finding, coincided with qualitative commentary, highlights some discrepancies linked to knowledge and understanding of PM within higher education and highlights areas where academics may need additional support.

conclusionThis article concludes by recognizing the challenges of delivering PM by some academics. It is noted that although the findings cannot be fully generalized, it does highlight fragmented understanding of PM among academic staff. This is important to reflect upon following the increasing requirements for radiography to become "more personalized."

Indexed as

CurriculumPrecision MedicineRadiographyHumansRadiologyUnited KingdomacademiaPersonalized medicinequalitativequantitativeradiographyradiotherapysurvey

Identifiers

PMID32620522
PMCPMC7327469

What OpenQuestion holds

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Registered trials

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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.