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Nutrition during pregnancy
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ISBN: 0309041384 9786610214310 1280214317 0309555582 0585037787 9780585037783 0309076765 9780309076760 9780309041386 Year: 1990 Publisher: Washington, D.C. National Academy Press

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Weight gain during pregnancy
Authors: --- ---
ISBN: 0309131138 0309131146 9780309131148 9780309131131 0309149150 Year: 2009 Publisher: Washington, D.C. National Academies Press

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"As women of childbearing age have become heavier, the trade-off between maternal and child health created by variation in gestational weight gain has become more difficult to reconcile. Weight Gain During Pregnancy responds to the need for a reexamination of the 1990 Institute of Medicine guidelines for weight gain during pregnancy. It builds on the conceptual framework that underscored the 1990 weight gain guidelines and addresses the need to update them through a comprehensive review of the literature and independent analyses of existing databases. The book explores relationships between weight gain during pregnancy and a variety of factors (e.g., the mother's weight and height before pregnancy) and places this in the context of the health of the infant and the mother, presenting specific, updated target ranges for weight gain during pregnancy and guidelines for proper measurement. New features of this book include a specific range of recommended gain for obese women. Weight Gain During Pregnancy is intended to assist practitioners who care for women of childbearing age, policy makers, educators, researchers, and the pregnant women themselves to understand the role of gestational weight gain and to provide them with the tools needed to promote optimal pregnancy outcomes."--Back cover.


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Big Data Analytics and Information Science for Business and Biomedical Applications II
Authors: ---
ISBN: 3036555501 3036555498 Year: 2022 Publisher: Basel MDPI - Multidisciplinary Digital Publishing Institute

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The analysis of big data in biomedical, business and financial research has drawn much attention from researchers worldwide. This collection of articles aims to provide a platform for an in-depth discussion of novel statistical methods developed for the analysis of Big Data in these areas. Both applied and theoretical contributions to these areas are showcased.

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