Challenges and implications of routine depression screening for depression in chronic disease and multimorbidity: a cross sectional study

<b>Background</b> Depression screening in chronic disease is advocated but its impact on routine practice is uncertain. We examine the effects of a programme of incentivised depression screening in chronic disease within a UK primary care setting.<p></p> <b>Methods and Findings</b> Cross sectional analysis of anonymised, routinely collected data (for 2008-9) from family practices in Scotland serving a population of circa 1.8 million. Patients registered in primary care with at least one of three chronic diseases, coronary heart disease, diabetes and stroke, underwent incentivised depression screening using the Hospital Anxiety and Depression Score (HADS). <p></p> 125143 patients were identified with at least one chronic disease. 10670 (8.5%) were under treatment for depression and exempt from screening. Of the remaining, HADS were recorded for 35537 (31.1%) patients. 7080 (19.9% of screened) had raised HADS (≥8); the majority had indications of mild depression with a HADS between 8 and 10. Over 6 months, 572 (8%) of those with a raised HADS (≥8) were initiated on antidepressants, while 696 (2.4%) patients with a normal HADS (<8) were also initiated on antidepressants (relative risk of antidepressant initiation with raised HADS 3.3 (CI 2.97-3.67), p value <0.0001). Of those with multimorbidity who were screened, 24.3% had a raised HADS (≥8). A raised HADS was more likely in females, socioeconomically deprived, multimorbid or younger (18-44) individuals. Females and 45-64 years old were more likely to receive antidepressants.<p></p> <b>Limitations</b> – retrospective study of routinely collected data.<p></p> <b>Conclusions </b> Despite incentivisation, only minority of patients underwent depression screening, suggesting that systematic depression screening in chronic disease can be difficult to achieve in routine practice. Targeting those at greatest risk such as the multimorbid or using simpler screening methods may be more effective. Raised HADS was associated with a higher number of new antidepressant prescriptions which has significant resource implications. The clinical benefits of such screening remain uncertain and merit investigation

Drug interactions may be important risk factors for methotrexate neurotoxicity, particularly in pediatric leukemia patients

Purpose: Methotrexate administration is associated with frequent adverse neurological events during treatment for childhood acute lymphoblastic leukemia. Here, we present evidence to support the role of common drug interactions and low vitamin B12 levels in potentiating methotrexate neurotoxicity. Methods: We review the published evidence and highlight key potential drug interactions as well as present clinical evidence of severe methotrexate neurotoxicity in conjunction with nitrous oxide anesthesia and measurements of vitamin B12 levels among pediatric leukemia patients during therapy. Results: We describe a very plausible mechanism for methotrexate neurotoxicity in pediatric leukemia patients involving reduction in methionine and consequential disruption of myelin production. We provide evidence that a number of commonly prescribed drugs in pediatric leukemia management interact with the same folate biosynthetic pathways and/or reduce functional vitamin B12 levels and hence are likely to increase the toxicity of methotrexate in these patients. We also present a brief case study supporting out hypothesis that nitrous oxide contributes to methotrexate neurotoxicity and a nutritional study, showing that patients. Conclusions: Use of nitrous oxide in pediatric leukemia patients at the same time as methotrexate use should be avoided especially as many suitable alternative anesthetic agents exist. Clinicians should consider monitoring levels of vitamin B12 in patients suspected of having methotrexate- induced neurotoxic effects

Reply: Methotrexate neurotoxicity due to drug interactions: an inadequate folinic acid effect

No abstract available

Field-calibrated model of melt, refreezing, and runoff for polar ice caps : Application to Devon Ice Cap

Acknowledgments R.M.M. was supported by the Scottish Alliance for Geoscience, Environment and Society (SAGES). The field data collection contributed to the validation of the European Space Agency Cryosat mission and was supported by the Natural Sciences and Engineering Research Council, Canada, the Meteorological Service of Canada (CRYSYS program), the Polar Continental Shelf Project (an agency of Natural Resources Canada), and by UK Natural Environment Research Council consortium grant NER/O/S/2003/00620. Support for D.O.B. was provided by the Canadian Circumpolar Institute and the Climate Change Geoscience Program, Earth Sciences Sector, Natural Resources Canada (ESS contribution 20130371). Thanks are also due to the Nunavut Research Institute and the communities of Resolute Bay and Grise Fjord for permission to conduct fieldwork on Devon Ice Cap. M.J. Sharp, A. Gardner, F. Cawkwell, R. Bingham, S. Williamson, L. Colgan, J. Davis, B. Danielson, J. Sekerka, L. Gray, and J. Zheng are thanked for logistical support and field assistance during the data collection. We thank Ruzica Dadic, two other anonymous reviewers, and the Editor, Bryn Hubbard, for their helpful comments on an earlier version of this paper and which resulted in significant improvements.Peer reviewedPublisher PD

Fluctuations of a Greenlandic tidewater glacier driven by changes in atmospheric forcing : observations and modelling of Kangiata Nunaata Sermia, 1859–present

Acknowledgements. The authors wish to thank Stephen Price, Mauri Pelto, and the anonymous reviewer for their reviews and comments that helped to improve the manuscript. RACMO2.1 data were provided by Jan van Angelen and Michiel van den Broeke, IMAU, Utrecht University. MAR v3.2 data used for runoff calculations were provided by Xavier Fettweis, Department of Geography, University of Liège. The photogrammetric DEM used in Figs. 1 and 3 was provided by Kurt H. Kjær, Centre for GeoGenetics, University of Copenhagen. This research was financially supported by J. M. Lea’s PhD funding, NERC grant number NE/I528742/1. Support for F. M. Nick was provided through the Conoco-Phillips/Lundin Northern Area Program CRIOS project (Calving Rates and Impact on Sea Level).Peer reviewedPublisher PD

Minibatch training of neural network ensembles via trajectory sampling

Most iterative neural network training methods use estimates of the loss function over small random subsets (or minibatches) of the data to update the parameters, which aid in decoupling the training time from the (often very large) size of the training datasets. Here, we show that a minibatch approach can also be used to train neural network ensembles (NNEs) via trajectory methods in a highly efficent manner. We illustrate this approach by training NNEs to classify images in the MNIST datasets. This method gives an improvement to the training times, allowing it to scale as the ratio of the size of the dataset to that of the average minibatch size which, in the case of MNIST, gives a computational improvement typically of two orders of magnitude. We highlight the advantage of using longer trajectories to represent NNEs, both for improved accuracy in inference and reduced update cost in terms of the samples needed in minibatch updates.Comment: 11 pages, 4 figures, 1 algorith

More on Multidimensional Scaling and Unfolding in R: smacof Version 2

The smacof package offers a comprehensive implementation of multidimensional scaling (MDS) techniques in R. Since its first publication (De Leeuw and Mair 2009b) the functionality of the package has been enhanced, and several additional methods, features and utilities were added. Major updates include a complete re-implementation of multidimensional unfolding allowing for monotone dissimilarity transformations, including row-conditional, circular, and external unfolding. Additionally, the constrained MDS implementation was extended in terms of optimal scaling of the external variables. Further package additions include various tools and functions for goodness-of-fit assessment, unidimensional scaling, gravity MDS, asymmetric MDS, Procrustes, and MDS biplots. All these new package functionalities are illustrated using a variety of real-life applications

Training neural network ensembles via trajectory sampling

In machine learning, there is renewed interest in neural network ensembles (NNEs), whereby predictions are obtained as an aggregate from a diverse set of smaller models, rather than from a single larger model. Here, we show how to define and train a NNE using techniques from the study of rare trajectories in stochastic systems. We define an NNE in terms of the trajectory of the model parameters under a simple, and discrete in time, diffusive dynamics, and train the NNE by biasing these trajectories towards a small time-integrated loss, as controlled by appropriate counting fields which act as hyperparameters. We demonstrate the viability of this technique on a range of simple supervised learning tasks. We discuss potential advantages of our trajectory sampling approach compared with more conventional gradient based methods.Comment: 12 pages, 5 figures, 1 appendi

Boundary conditions dependence of the phase transition in the quantum Newman-Moore model

We study the triangular plaquette model (TPM, also known as the Newman-Moore model) in the presence of a transverse magnetic field on a lattice with periodic boundaries in both spatial dimensions. We consider specifically the approach to the ground state phase transition of this quantum TPM (QTPM, or quantum Newman-Moore model) as a function of the system size and type of boundary conditions. Using cellular automata methods, we obtain a full characterization of the minimum energy configurations of the TPM for arbitrary tori sizes. For the QTPM, we use these cycle patterns to obtain the symmetries of the model, which we argue determine its quantum phase transition: we find it to be a first-order phase transition, with the addition of spontaneous symmetry breaking for system sizes which have degenerate classical ground states. For sizes accessible to numerics, we also find that this classification is consistent with exact diagonalization, Matrix Product States and Quantum Monte Carlo simulations.Comment: fixed unclear point, given the correct credit to citatio