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R-IDEAL: A Framework for Systematic Clinical Evaluation of Technical Innovations in Radiation Oncology

Overview of attention for article published in Frontiers in oncology, April 2017
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (86th percentile)
  • High Attention Score compared to outputs of the same age and source (87th percentile)

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24 X users

Citations

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Title
R-IDEAL: A Framework for Systematic Clinical Evaluation of Technical Innovations in Radiation Oncology
Published in
Frontiers in oncology, April 2017
DOI 10.3389/fonc.2017.00059
Pubmed ID
Authors

Helena M. Verkooijen, Linda G. W. Kerkmeijer, Clifton D. Fuller, Robbert Huddart, Corinne Faivre-Finn, Marcel Verheij, Stella Mook, Arjun Sahgal, Emma Hall, Chris Schultz

Abstract

The pace of innovation in radiation oncology is high and the window of opportunity for evaluation narrow. Financial incentives, industry pressure, and patients' demand for high-tech treatments have led to widespread implementation of innovations before, or even without, robust evidence of improved outcomes has been generated. The standard phase I-IV framework for drug evaluation is not the most efficient and desirable framework for assessment of technological innovations. In order to provide a standard assessment methodology for clinical evaluation of innovations in radiotherapy, we adapted the surgical IDEAL framework to fit the radiation oncology setting. Like surgery, clinical evaluation of innovations in radiation oncology is complicated by continuous technical development, team and operator dependence, and differences in quality control. Contrary to surgery, radiotherapy innovations may be used in various ways, e.g., at different tumor sites and with different aims, such as radiation volume reduction and dose escalation. Also, the effect of radiation treatment can be modeled, allowing better prediction of potential benefits and improved patient selection. Key distinctive features of R-IDEAL include the important role of predicate and modeling studies (Stage 0), randomization at an early stage in the development of the technology, and long-term follow-up for late toxicity. We implemented R-IDEAL for clinical evaluation of a recent innovation in radiation oncology, the MRI-guided linear accelerator (MR-Linac). MR-Linac combines a radiotherapy linear accelerator with a 1.5-T MRI, aiming for improved targeting, dose escalation, and margin reduction, and is expected to increase the use of hypofractionation, improve tumor control, leading to higher cure rates and less toxicity. An international consortium, with participants from seven large cancer institutes from Europe and North America, has adopted the R-IDEAL framework to work toward coordinated, evidence-based introduction of the MR-Linac. R-IDEAL holds the promise for timely, evidence-based introduction of radiotherapy innovations with proven superior effectiveness, while preventing unnecessary exposure of patients to potentially harmful interventions.

X Demographics

X Demographics

The data shown below were collected from the profiles of 24 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 124 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 <1%
Unknown 123 99%

Demographic breakdown

Readers by professional status Count As %
Researcher 24 19%
Student > Ph. D. Student 21 17%
Student > Master 16 13%
Student > Bachelor 8 6%
Other 7 6%
Other 25 20%
Unknown 23 19%
Readers by discipline Count As %
Medicine and Dentistry 44 35%
Physics and Astronomy 13 10%
Nursing and Health Professions 9 7%
Business, Management and Accounting 4 3%
Psychology 3 2%
Other 11 9%
Unknown 40 32%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 15. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 02 October 2022.
All research outputs
#2,402,497
of 25,382,440 outputs
Outputs from Frontiers in oncology
#555
of 22,428 outputs
Outputs of similar age
#43,913
of 323,671 outputs
Outputs of similar age from Frontiers in oncology
#9
of 74 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 22,428 research outputs from this source. They receive a mean Attention Score of 3.0. This one has done particularly well, scoring higher than 97% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 323,671 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 86% of its contemporaries.
We're also able to compare this research output to 74 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 87% of its contemporaries.