Evidence map›Paper›PMID 26881200›Full record

ArticleBioMed research international2015

Assessment of Random Error in Phantom Dosimetry with the Use of Error Simulation in Statistical Software.

R C Hoogeveen, E P Martens, P F van der Stelt, W E R Berkhout

Abstract read
In one paragraph

Article in BioMed research international, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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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

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No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

4 authors.

R C HoogeveenDepartment of Oral and Maxillofacial Radiology, Academic Center for Dentistry Amsterdam (ACTA), Gustav Mahlerlaan 3004, 1081 LA Amsterdam, Netherlands.
E P MartensStatisticor, Statistical Research Office, Dorpsstraat 90, 2831 AT Gouderak, Netherlands.
P F van der SteltDepartment of Oral and Maxillofacial Radiology, Academic Center for Dentistry Amsterdam (ACTA), Gustav Mahlerlaan 3004, 1081 LA Amsterdam, Netherlands.
W E R BerkhoutDepartment of Oral and Maxillofacial Radiology, Academic Center for Dentistry Amsterdam (ACTA), Gustav Mahlerlaan 3004, 1081 LA Amsterdam, Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo investigate if software simulation is practical for quantifying random error (RE) in phantom dosimetry. MATERIALS AND

methodsWe applied software error simulation to an existing dosimetry study. The specifications and the measurement values of this study were brought into the software (R version 3.0.2) together with the algorithm of the calculation of the effective dose (E). Four sources of RE were specified: (1) the calibration factor; (2) the background radiation correction; (3) the read-out process of the dosimeters; and (4) the fluctuation of the X-ray generator.

resultsThe amount of RE introduced by these sources was calculated on the basis of the experimental values and the mathematical rules of error propagation. The software repeated the calculations of E multiple times (n = 10,000) while attributing the applicable RE to the experimental values. A distribution of E emerged as a confidence interval around an expected value.

conclusionsCredible confidence intervals around E in phantom dose studies can be calculated by using software modelling of the experiment. With credible confidence intervals, the statistical significance of differences between protocols can be substantiated or rejected. This modelling software can also be used for a power analysis when planning phantom dose experiments.

Indexed as

Phantoms, ImagingSoftwareComputer SimulationRadiometry

Identifiers

PMID26881200
PMCPMC4736220

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