Search PubMed⌕ Search
Home/Archive/PubMed

PubMed · 1594846

[Pediatric emergencies].

S Fanconi·J Uehlinger
View original source ↗Export metadata ↓

Abstract

We describe the most frequent emergencies in pediatrics and discuss their differential diagnosis and therapy. Dyspnea, shock, coma, convulsions, infectious CNS affections, head injury and burns are reported in detail. The importance of correct diagnosis and correct clinical assessment is emphasized, as they influence therapy and further management of the patients.

Explore related subjects

Brain DiseasesBrain InjuriesBurnsChildComaDyspneaEmergenciesHeart ArrestHumansPediatricsSeizures, FebrileShockTransportation of PatientsEnglish AbstractJournal Article

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

S Fanconi, J Uehlinger. 1992-04-28. [Pediatric emergencies].. https://pubmed.ncbi.nlm.nih.gov/1594846/

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2007

A penalized likelihood approach to magnetic resonance image reconstruction.

Currently, images acquired via magnetic resonance imaging (MRI) and functional magnetic resonance imaging (fMRI) technology are reconstructed using the discrete inverse Fourier transform. While computationally convenient, this approach is not able to filter out noise. This is a serious limitation because the amount of noise in MRI and fMRI can be substantial. In this paper, we propose an alternative approach to reconstruction, based on penalized likelihood methodology. In particular, we focus on non-linear shrinkage estimators and show that this approach achieves a great reduction in integrated mean squared error (IMSE) of the estimated image with respect to the currently used estimator. This approach is extremely fast and easy to implement computationally. In addition, it can be combined with various alternative approaches to MR image reconstruction and can be easily adapted to other, non-MRI contexts, in which the observed data and the quantities of interest are related via a linear transform.

Brain Diseases↗