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...
| Publicado en: | Journal of Clinical Research in Pediatric Endocrinology Vol. 13; no. 2; pp. 124 - 136 |
|---|---|
| Autores principales: | , , , , , , , |
| Formato: | review Journal Article |
| Publicado: |
Galenos Yayinevi Tic. LTD. STI
Jun2021
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=150805999&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 150805999 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13085727 8X6R jtl: Journal of Clinical Research in Pediatric Endocrinology issn: 13085727 maglogo: N pubinfo: dt: Jun2021 vid: 13 iid: 2 pid: 28155 pub: Galenos Yayinevi Tic. LTD. STI artinfo: ui: 150805999 150805999 150805999 10.4274/jcrpe.galenos.2020.2020.0206 150805999 ppf: 124 ppct: 12 formats: tig: atl: Important Tools for Use by Pediatric Endocrinologists in the Assessment of Short Stature. aug: au: Labarta, José I. Ranke, Michael B. Maghnie, Mohamad Martin, David Guazzarotti, Laura Pfäffle, Roland Koledova, Ekaterina Wit, Jan M. affil: University of Zaragoza, Children's Hospital Miguel Servet, Instituto de Investigación Sanitaria de Aragón, Unit of Endocrinology, Zaragoza, Spain sug: subj: Pediatricians Endocrinologists Body Height Evaluation Prediction Models Utilization Growth Disorders Diagnosis Growth Disorders Risk Factors Risk Assessment Digital Technology Algorithms Sensitivity and Specificity Child Health Age Determination by Skeleton Human Growth Hormone Therapeutic Use Software Diffusion of Innovation Skull Deep Learning Neural Networks (Computer) Growth Disorders Treatment Outcomes Evaluation Artificial Intelligence Somatomedins Analysis Technology, Medical Quality Improvement Congresses and Conferences Italy Italy Human Growth Hormone Deficiency ab: 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. pubtype: Academic Journal doctype: review Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|