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A unique method for estimating the reliability learning curve of optic nerve sheath diameter ultrasound measurement

Overview of attention for article published in The Ultrasound Journal, August 2016
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Title
A unique method for estimating the reliability learning curve of optic nerve sheath diameter ultrasound measurement
Published in
The Ultrasound Journal, August 2016
DOI 10.1186/s13089-016-0044-x
Pubmed ID
Authors

Frederick A. Zeiler, Markus T. Ziesmann, Patrick Goeres, Bertram Unger, Jason Park, Dimitrios Karakitsos, Michael Blaivas, Ashley Vergis, Lawrence M. Gillman

Abstract

Optic nerve sheath diameter (ONSD) measurement using ultrasound has been proposed as a rapid, non-invasive, point of care technique to estimate intra-cranial pressure (ICP). Ultrasonic measurement of the optic nerve sheath can be quite challenging and there is limited literature surrounding learning curves for this technique. We attempted to develop a method to estimate the reliability learning curve for ONSD measurement utilizing a unique definition of reliability: a plateau in within-subject variability with unchanged between-subject variability. As part of a previously published study, a single operator measured the ONSD in 120 healthy volunteers over a 6-month period. Utilizing the assumption that the four measurements made on each subject during this study should be equal, the relationship of within-subject variance was described using a quadratic-plateau model as assessed by segmental polynomial (knot) regression. Segmental polynomial (knot) regression revealed a plateau in within-subject variance after the 21st subject. However, there was no difference in overall mean values [3.69 vs 3.68 mm (p = 0.884)] or between-subject variance [14.49 vs 11.92 (p = 0.54)] above or below this cutoff. This study suggests a significant finite learning curve associated with ONSD measurements. It also offers a unique method of calculating the learning curve associated with ONSD measurement.

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Geographical breakdown

Country Count As %
Japan 1 2%
Unknown 52 98%

Demographic breakdown

Readers by professional status Count As %
Other 6 11%
Student > Master 6 11%
Researcher 5 9%
Student > Postgraduate 5 9%
Student > Bachelor 5 9%
Other 15 28%
Unknown 11 21%
Readers by discipline Count As %
Medicine and Dentistry 27 51%
Engineering 2 4%
Biochemistry, Genetics and Molecular Biology 1 2%
Business, Management and Accounting 1 2%
Nursing and Health Professions 1 2%
Other 5 9%
Unknown 16 30%