De arts als direktief therapeut : een onderzoek naar de toepassing van direktieve therapie door de huisarts

In dit onderzoek wordt nagegaan of en in hoeverre patiënten met psychosociale problemen in een eerstelijns-kontekst, c.q. de huisarts, adekwaat behandeld kunnen worden door middel van technieken uit de direktieve therapie. Onder 'direktieven' verstaan we aanwijzingen, adviezen en opdrachten, die toegepast kunnen worden binnen de psychologische hulpverlening, om problemen te verhelderen en veranderingen te bewerkstelligen. Geïnspireerd door de bevinding dat studenten klinische psychologie, na het volgen van een kursus direktieve therapie, goed overweg konden met de behandeling van kliënten met psychosociale problemen, afkomstig uit diverse huisartsenpraktijken, werd de vraagstelling geformuleerd of en in hoeverre huisartsen na het volgen van een kursus direktieve therapie, in staat waren deze vorm van therapie zelf toe te passen. De aan het onderzoek deelnemende huisartsen participeerden gedurende de onderzoeksperiade in zgn. intervisiegroepe

A characteristic particle method for traffic flow simulations on highway networks

A characteristic particle method for the simulation of first order macroscopic traffic models on road networks is presented. The approach is based on the method "particleclaw", which solves scalar one dimensional hyperbolic conservations laws exactly, except for a small error right around shocks. The method is generalized to nonlinear network flows, where particle approximations on the edges are suitably coupled together at the network nodes. It is demonstrated in numerical examples that the resulting particle method can approximate traffic jams accurately, while only devoting a few degrees of freedom to each edge of the network.Comment: 15 pages, 5 figures. Accepted to the proceedings of the Sixth International Workshop Meshfree Methods for PDE 201

Automated assessment of COVID-19 reporting and data system and chest CT severity scores in patients suspected of having COVID-19 using artificial intelligence

Background: The coronavirus disease 2019 (COVID-19) pandemic has spread across the globe with alarming speed, morbidity, and mortality. Immediate triage of patients with chest infections suspected to be caused by COVID-19 using chest CT may be of assistance when results from definitive viral testing are delayed.Purpose: To develop and validate an artificial intelligence (AI) system to score the likelihood and extent of pulmonary COVID-19 on chest CT scans using the COVID-19 Reporting and Data System (CO-RADS) and CT severity scoring systems.Materials and Methods: The CO-RADS AI system consists of three deep-learning algorithms that automatically segment the five pulmonary lobes, assign a CO-RADS score for the suspicion of COVID-19, and assign a CT severity score for the degree of parenchymal involvement per lobe. This study retrospectively included patients who underwent a nonenhanced chest CT examination because of clinical suspicion of COVID-19 at two medical centers. The system was trained, validated, and tested with data from one of the centers. Data from the second center served as an external test set. Diagnostic performance and agreement with scores assigned by eight independent observers were measured using receiver operating characteristic analysis, linearly weighted kappa values, and classification accuracy.Results: A total of 105 patients (mean age, 62 years +/- 16 [standard deviation]; 61 men) and 262 patients (mean age, 64 years +/- 16; 154 men) were evaluated in the internal and external test sets, respectively. The system discriminated between patients with COVID-19 and those without COVID-19, with areas under the receiver operating characteristic curve of 0.95 (95% CI: 0.91, 0.98) and 0.88 (95% CI: 0.84, 0.93), for the internal and external test sets, respectively. Agreement with the eight human observers was moderate to substantial, with mean linearly weighted k values of 0.60 +/- 0.01 for CO-RADS scores and 0.54 +/- 0.01 for CT severity scores.Conclusion: With high diagnostic performance, the CO-RADS AI system correctly identified patients with COVID-19 using chest CT scans and assigned standardized CO-RADS and CT severity scores that demonstrated good agreement with findings from eight independent observers and generalized well to external data. (C) RSNA, 2020Cardiovascular Aspects of Radiolog

Height and wavelength of alternate bars in rivers: modelling vs. laboratory experiments

Alternate bars are large wave patterns in sandy beds of rivers and channels. The crests and troughs alternate between the banks of the channel. These bars, which move downstream several meters per day, reduce the navigability of the river. Recent modelling of alternate bars has focused on stability analysis techniques. We think, that the resulting models can predict large rhythmic patterns in sandy beds, especially if the models can be combined with data-assimilation techniques. The results presented in this paper confirm this thought. We compared the wavelength and height of alternate bars as predicted by the model of Schielen et al. [14], with the values measured in several flume experiments. Given realistic hydraulic conditions R root Re &gt; 2*10(3), (R the width-to-depth ratio and R, the Reynolds number), the predictions are in good agreement with the measurements. In addition, the model predicts the bars measured in experiments with graded sediment. If R root Re &lt; 2*103, the agreement between model results and measurements is lost. The wave height is clearly underestimated, and the standard deviation of the differences between predictions and measurements increases. This questions the usefulness of small flume experiments for morphodynamic problems.<br/