All-sky Medium Energy Gamma-ray Observatory: Exploring the Extreme Multimessenger Universe

The All-sky Medium Energy Gamma-ray Observatory (AMEGO) is a probe class mission concept that will provide essential contributions to multimessenger astrophysics in the late 2020s and beyond. AMEGO combines high sensitivity in the 200 keV to 10 GeV energy range with a wide field of view, good spectral resolution, and polarization sensitivity. Therefore, AMEGO is key in the study of multimessenger astrophysical objects that have unique signatures in the gamma-ray regime, such as neutron star mergers, supernovae, and flaring active galactic nuclei. The order-of-magnitude improvement compared to previous MeV missions also enables discoveries of a wide range of phenomena whose energy output peaks in the relatively unexplored medium-energy gamma-ray band

Enough Power to Build a Strong Case for Clinical Pharmacy Services?

To the Editor We read with great interest the Original Investigation by Ravn-Nielsen et al1 published in a recent issue of JAMA Internal Medicine. Owing to the large size (n = 1467) and randomized controlled design, the results of the OPTIMIST study1 seem valid and should be considered to be of particular importance to facilitate the further implementation of in-hospital clinical pharmacy services. Ravn-Nielsen et al1 showed that a multifaceted pharmacist intervention, initiated during hospital stay and including a motivational patient interview and follow-up after discharge, significantly reduced the number of all-cause readmissions in a Danish patient sample (number needed to treat = 12). The number of drug-related readmissions was not affected significantly, however. These findings are in contrast with previous investigations by Gillespie et al2 and Pellegrin et al,3 of which both studies found lower rates of drug-related readmissions without a difference in the number of all-cause readmissions.status: publishe

Nominations, ratings, and the dimensions of sociometric status

In 1944, U. Bronfenbrenner remarked on the need for a two-dimensional model of sociometric status. The low value of the correlation between the variables liking and disliking-assumed basic dimensions of sociometric status-is often cited as evidence for the correctness of Bronfenbrenner’ssuggestion. Sociometric status is derived from a coalescence of judgements at the individual level. In this article we argue that score attribution at this level (where one group member assesses another) is one-dimensional along the liking-disliking continuum. Two-dimensionality of sociometric status arises at the group level. However, we also show that at this level liking and disliking are not two distinct dimensions, but the poles of just one, the other being visibility (or impact). If the one-dimensional model of liking score attribution on the individual level is accepted, the obvious thing to do is to instruct respondents accordingly. Rating scales are suitable for this. The rating-scale methods we suggested in previous publications (e.g. Maassen, Akkermans, & van der Linden, 1996) are in keeping with this argument. Moreover, these methods may be recommended for their reliability, validity and for the variety of research designs to which they can be applied

Two-dimensional sociometric status determination with rating scales

Sociometric status is derivedfrom a concatenation ofjudgments at the individual leveL In previous articles, the authors argued that score attribution at this level (where one group member assesses another) is one-dimensional along the sympathy+-antipathy continuum. Two-dimensionality of sociometric status arises at the group level. It was shown that at this level, too, sympathy and antipathy are not two distinct dimensions but the poles ofjust one, the other being visibility (or impact). If one accepts the model of one-dimensional score attribution at the individual level, it would seem logical to base sociometric status determination on rating scales. In this article, a procedure for this is developed and a covering computer program (SSRAT) is introduced. Finally, the results of the current nomination methods and the proposed rating method applied in the same classroom groups are compared The results of the rating method appear to be more valid and more refined

Rationalisatie van slaapmiddelen: een sisyfusarbeid?

Ondanks meerdere sensibilisatiecampagnes gedurende de afgelopen jaren blijft langdurig gebruik van slaapmiddelen een prevalent probleem. Gezien de snelle tolerantie voor het effect in combinatie met de bijwerkingen is langdurig gebruik veelal schadelijk voor de patiënt. Indien er geen andere mogelijkheid is dan het gebruik van slaapmiddelen, moet men de duur beperken tot twee weken en moet er systematisch getracht worden om bij langdurig gebruik deze middelen af te bouwen en finaal te stoppen. De afbouw van slaapmiddelen is een uitdaging gezien de psychologische en de fysieke afhankelijkheid aan deze middelen. De winst na afbouw treedt echter snel op. Bij het gebruik van deze geneesmiddelen bij ouderen is het risico op ongewenste effecten groter, met name een verminderd cognitief vermogen en verminderde motorische functies, met als gevolg een grotere kans op ongewenste sedatie, vallen en fracturen.status: publishe

Diagnosis of early stage knee osteoarthritis based on early clinical course: data from the CHECK cohort

Background: Early diagnosis of knee osteoarthritis (OA) is important in managing this disease, but such an early diagnostic tool is still lacking in clinical practice. The purpose of this study was to develop diagnostic models for early stage knee OA based on the first 2-year clinical course after the patient’s initial presentation in primary care and to identify whether these course factors had additive discriminative value over baseline factors. Methods: We extracted eligible patients’ clinical and radiographic data from the CHECK cohort and formed the first 2-year course factors according to the factors’ changes over the 2 years. Clinical expert consensus-based diagnosis, which was made via evaluating patients’ 5- to 10-year follow-up data, was used as the outcome factor. Four models were developed: model 1, included clinical course factors only; model 2, included clinical and radiographic course factors; model 3, clinical baseline factors + clinical course factors; and model 4, clinical and radiographic baseline factors + clinical and radiographic course factors. All the models were built by a generalized estimating equation with a backward selection method. Area under the receiver operating characteristic curve (AUC) and its 95% confidence interval (CI) were calculated for assessing model discrimination. Delong’s method compared AUCs. Results: Seven hundred sixty-one patients with 1185 symptomatic knees were included in this study. Thirty-seven percent knees were diagnosed as OA at follow-up. Model 1 contained 6 clinical course factors; model 2: 6 clinical and 3 radiographic course factors; model 3: 6 baseline clinical factors combined with 5 clinical course factors; and model 4: 4 clinical and 1 radiographic baseline factors combined with 5 clinical and 3 radiographic course factors. Model discriminations are as follows: model 1, AUC 0.70 (95% CI 0.67–0.74); model 2, 0.74 (95% CI 0.71–0.77); model 3, 0.77 (95% CI 0.74–0.80); and model 4, 0.80 (95% CI 0.77–0.82). AUCs of model 3 and model 4 were slightly but significantly higher than corresponding baseline-factor models (model 3 0.77 vs 0.75, p = 0.031; model 4 0.80 vs 0.76, p = 0.003). Conclusions: Four diagnostic models were developed with “fair” to “good” discriminations. First 2-year course factors had additive discriminative value over baseline factors