Thromboembolic Risk in Juvenile Dermatomyositis Patients Treated With Intravenous Immunoglobulin.

This single-institution retrospective cohort study evaluates the risk of thromboembolic events (TE) in patients with juvenile dermatomyositis (JDM) treated with intravenous immunoglobulin (IVIg). We found no difference in the occurrence of TE in JDM patients treated with IVIg as compared to those without IVIg exposure; both cohorts had no TEs. Patients in both JDM cohorts had matched levels of other clotting risk factors, such as periods of limited mobility and hormonal contraceptive use.

Disentangling Sources of Momentum Fluctuations in Xe+Xe and Pb+Pb Collisions with the ATLAS Detector.

High-energy nuclear collisions create a quark-gluon plasma, whose initial condition and subsequent expansion vary from event to event, impacting the distribution of the eventwise average transverse momentum [P([p_{T}])]. Disentangling the contributions from fluctuations in the nuclear overlap size (geometrical component) and other sources at a fixed size (intrinsic component) remains a challenge. This problem is addressed by measuring the mean, variance, and skewness of P([p_{T}]) in ^{208}Pb+^{208}Pb and ^{129}Xe+^{129}Xe collisions at sqrt[s_{NN}]=5.

How does organisational culture facilitate uptake of home dialysis? An ethnographic study of kidney centres in England.

Objective: The proportion of people having home dialysis for kidney disease varies considerably by treating centre, socioeconomic deprivation levels in the area and to some extent ethnicity. This study aimed to gain in-depth insights into cultural and organisational factors contributing to this variation in uptake.

Design: This is the first ethnographic study of kidney centre culture to focus on home dialysis uptake.

Daily automated prediction of delirium risk in hospitalized patients: Model development and validation.

Background: Delirium is common in hospitalized patients and correlated with increased morbidity and mortality. Despite this, delirium is underdiagnosed, and many institutions do not have sufficient resources to consistently apply effective screening and prevention.

Objective: To develop a machine learning algorithm to identify patients at highest risk of delirium in the hospital each day in an automated fashion based on data available in the electronic medical record, reducing the barrier to large-scale delirium screening.