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Important Tools for Use by Pediatric Endocrinologists in the Assessment of Short Stature
Assessment and management of children with growth failure has improved greatly over recent years. However, there remains a strong potential for further improvements by using novel digital techniques. A panel of experts discussed developments in digitalization of a number of important tools used by p...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Galenos Publishing
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8186334/ https://www.ncbi.nlm.nih.gov/pubmed/33006554 http://dx.doi.org/10.4274/jcrpe.galenos.2020.2020.0206 |
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author | Labarta, José I. Ranke, Michael B. Maghnie, Mohamad Martin, David Guazzarotti, Laura Pfäffle, Roland Koledova, Ekaterina Wit, Jan M. |
author_facet | Labarta, José I. Ranke, Michael B. Maghnie, Mohamad Martin, David Guazzarotti, Laura Pfäffle, Roland Koledova, Ekaterina Wit, Jan M. |
author_sort | Labarta, José I. |
collection | PubMed |
description | Assessment and management of children with growth failure has improved greatly over recent years. However, there remains a strong potential for further improvements by using novel digital techniques. A panel of experts discussed developments in digitalization of a number of important tools used by pediatric endocrinologists at the third 360° European Meeting on Growth and Endocrine Disorders, funded by Merck KGaA, Germany, and this review is based on those discussions. It was reported that electronic monitoring and new algorithms have been devised that are providing more sensitive referral for short stature. In addition, computer programs have improved ways in which diagnoses are coded for use by various groups including healthcare providers and government health systems. Innovative cranial imaging techniques have been devised that are considered safer than using gadolinium contrast agents and are also more sensitive and accurate. Deep-learning neural networks are changing the way that bone age and bone health are assessed, which are more objective than standard methodologies. Models for prediction of growth response to growth hormone (GH) treatment are being improved by applying novel artificial intelligence methods that can identify non-linear and linear factors that relate to response, providing more accurate predictions. Determination and interpretation of insulin-like growth factor-1 (IGF-1) levels are becoming more standardized and consistent, for evaluation across different patient groups, and computer-learning models indicate that baseline IGF-1 standard deviation score is among the most important indicators of GH therapy response. While physicians involved in child growth and treatment of disorders resulting in growth failure need to be aware of, and keep abreast of, these latest developments, treatment decisions and management should continue to be based on clinical decisions. New digital technologies and advancements in the field should be aimed at improving clinical decisions, making greater standardization of assessment and facilitating patient-centered approaches. |
format | Online Article Text |
id | pubmed-8186334 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Galenos Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-81863342021-06-17 Important Tools for Use by Pediatric Endocrinologists in the Assessment of Short Stature Labarta, José I. Ranke, Michael B. Maghnie, Mohamad Martin, David Guazzarotti, Laura Pfäffle, Roland Koledova, Ekaterina Wit, Jan M. J Clin Res Pediatr Endocrinol Review Assessment and management of children with growth failure has improved greatly over recent years. However, there remains a strong potential for further improvements by using novel digital techniques. A panel of experts discussed developments in digitalization of a number of important tools used by pediatric endocrinologists at the third 360° European Meeting on Growth and Endocrine Disorders, funded by Merck KGaA, Germany, and this review is based on those discussions. It was reported that electronic monitoring and new algorithms have been devised that are providing more sensitive referral for short stature. In addition, computer programs have improved ways in which diagnoses are coded for use by various groups including healthcare providers and government health systems. Innovative cranial imaging techniques have been devised that are considered safer than using gadolinium contrast agents and are also more sensitive and accurate. Deep-learning neural networks are changing the way that bone age and bone health are assessed, which are more objective than standard methodologies. Models for prediction of growth response to growth hormone (GH) treatment are being improved by applying novel artificial intelligence methods that can identify non-linear and linear factors that relate to response, providing more accurate predictions. Determination and interpretation of insulin-like growth factor-1 (IGF-1) levels are becoming more standardized and consistent, for evaluation across different patient groups, and computer-learning models indicate that baseline IGF-1 standard deviation score is among the most important indicators of GH therapy response. While physicians involved in child growth and treatment of disorders resulting in growth failure need to be aware of, and keep abreast of, these latest developments, treatment decisions and management should continue to be based on clinical decisions. New digital technologies and advancements in the field should be aimed at improving clinical decisions, making greater standardization of assessment and facilitating patient-centered approaches. Galenos Publishing 2021-06 2021-06-02 /pmc/articles/PMC8186334/ /pubmed/33006554 http://dx.doi.org/10.4274/jcrpe.galenos.2020.2020.0206 Text en ©Copyright 2021 by Turkish Pediatric Endocrinology and Diabetes Society | The Journal of Clinical Research in Pediatric Endocrinology published by Galenos Publishing House. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Review Labarta, José I. Ranke, Michael B. Maghnie, Mohamad Martin, David Guazzarotti, Laura Pfäffle, Roland Koledova, Ekaterina Wit, Jan M. Important Tools for Use by Pediatric Endocrinologists in the Assessment of Short Stature |
title | Important Tools for Use by Pediatric Endocrinologists in the Assessment of Short Stature |
title_full | Important Tools for Use by Pediatric Endocrinologists in the Assessment of Short Stature |
title_fullStr | Important Tools for Use by Pediatric Endocrinologists in the Assessment of Short Stature |
title_full_unstemmed | Important Tools for Use by Pediatric Endocrinologists in the Assessment of Short Stature |
title_short | Important Tools for Use by Pediatric Endocrinologists in the Assessment of Short Stature |
title_sort | important tools for use by pediatric endocrinologists in the assessment of short stature |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8186334/ https://www.ncbi.nlm.nih.gov/pubmed/33006554 http://dx.doi.org/10.4274/jcrpe.galenos.2020.2020.0206 |
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