Challenges in developing prediction models for stillbirth.

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Accepted Manuscript (AM)
Author(s)
Sexton, Jessica
Ellwood, David
Flenady, Vicki
Griffith University Author(s)
Year published
2020
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Stillbirth is a major global public health problem, but in the absence of a robust method to predict a woman's individualized risk of stillbirth, identifying women at increased risk remains a challenge. Awareness of factors that increase risk is a necessary step in improving care through better communication and shared decision-making, with the goal of reducing stillbirths. Despite a high proportion of unexplained stillbirths, many women have one or more risk factors that are often unrecognized (Flenady, V. et al Lancet 2011, 377, 1331-40).Stillbirth is a major global public health problem, but in the absence of a robust method to predict a woman's individualized risk of stillbirth, identifying women at increased risk remains a challenge. Awareness of factors that increase risk is a necessary step in improving care through better communication and shared decision-making, with the goal of reducing stillbirths. Despite a high proportion of unexplained stillbirths, many women have one or more risk factors that are often unrecognized (Flenady, V. et al Lancet 2011, 377, 1331-40).
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Journal Title
BJOG
Copyright Statement
© 2020 RCOG. This is the peer reviewed version of the following article: Challenges in developing prediction models for stillbirth, BJOG: An International Journal of Obstetrics and Gynaecology, Accepted Articles, 2020, which has been published in final form at 10.1111/1471-0528.16525. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving (http://olabout.wiley.com/WileyCDA/Section/id-828039.html)
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This publication has been entered into Griffith Research Online as an Advanced Online Version.
Subject
Biomedical and clinical sciences
Clinical sciences