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Only a limited number of patients with major depressive disorder (MDD) respond to a first course of antidepressant medication (ADM). We investigated the feasibility of creating a baseline model to determine which of these would be among patients beginning ADM treatment in the US Veterans Health Administration (VHA).
Methods
A 2018–2020 national sample of n = 660 VHA patients receiving ADM treatment for MDD completed an extensive baseline self-report assessment near the beginning of treatment and a 3-month self-report follow-up assessment. Using baseline self-report data along with administrative and geospatial data, an ensemble machine learning method was used to develop a model for 3-month treatment response defined by the Quick Inventory of Depression Symptomatology Self-Report and a modified Sheehan Disability Scale. The model was developed in a 70% training sample and tested in the remaining 30% test sample.
Results
In total, 35.7% of patients responded to treatment. The prediction model had an area under the ROC curve (s.e.) of 0.66 (0.04) in the test sample. A strong gradient in probability (s.e.) of treatment response was found across three subsamples of the test sample using training sample thresholds for high [45.6% (5.5)], intermediate [34.5% (7.6)], and low [11.1% (4.9)] probabilities of response. Baseline symptom severity, comorbidity, treatment characteristics (expectations, history, and aspects of current treatment), and protective/resilience factors were the most important predictors.
Conclusions
Although these results are promising, parallel models to predict response to alternative treatments based on data collected before initiating treatment would be needed for such models to help guide treatment selection.
Fewer than half of patients with major depressive disorder (MDD) respond to psychotherapy. Pre-emptively informing patients of their likelihood of responding could be useful as part of a patient-centered treatment decision-support plan.
Methods
This prospective observational study examined a national sample of 807 patients beginning psychotherapy for MDD at the Veterans Health Administration. Patients completed a self-report survey at baseline and 3-months follow-up (data collected 2018–2020). We developed a machine learning (ML) model to predict psychotherapy response at 3 months using baseline survey, administrative, and geospatial variables in a 70% training sample. Model performance was then evaluated in the 30% test sample.
Results
32.0% of patients responded to treatment after 3 months. The best ML model had an AUC (SE) of 0.652 (0.038) in the test sample. Among the one-third of patients ranked by the model as most likely to respond, 50.0% in the test sample responded to psychotherapy. In comparison, among the remaining two-thirds of patients, <25% responded to psychotherapy. The model selected 43 predictors, of which nearly all were self-report variables.
Conclusions
Patients with MDD could pre-emptively be informed of their likelihood of responding to psychotherapy using a prediction tool based on self-report data. This tool could meaningfully help patients and providers in shared decision-making, although parallel information about the likelihood of responding to alternative treatments would be needed to inform decision-making across multiple treatments.
The remnant phase of a radio galaxy begins when the jets launched from an active galactic nucleus are switched off. To study the fraction of radio galaxies in a remnant phase, we take advantage of a $8.31$ deg$^2$ subregion of the GAMA 23 field which comprises of surveys covering the frequency range 0.1–9 GHz. We present a sample of 104 radio galaxies compiled from observations conducted by the Murchison Widefield Array (216 MHz), the Australia Square Kilometer Array Pathfinder (887 MHz), and the Australia Telescope Compact Array (5.5 GHz). We adopt an ‘absent radio core’ criterion to identify 10 radio galaxies showing no evidence for an active nucleus. We classify these as new candidate remnant radio galaxies. Seven of these objects still display compact emitting regions within the lobes at 5.5 GHz; at this frequency the emission is short-lived, implying a recent jet switch off. On the other hand, only three show evidence of aged lobe plasma by the presence of an ultra-steep-spectrum ($\alpha<-1.2$) and a diffuse, low surface brightness radio morphology. The predominant fraction of young remnants is consistent with a rapid fading during the remnant phase. Within our sample of radio galaxies, our observations constrain the remnant fraction to $4\%\lesssim f_{\mathrm{rem}} \lesssim 10\%$; the lower limit comes from the limiting case in which all remnant candidates with hotspots are simply active radio galaxies with faint, undetected radio cores. Finally, we model the synchrotron spectrum arising from a hotspot to show they can persist for 5–10 Myr at 5.5 GHz after the jets switch of—radio emission arising from such hotspots can therefore be expected in an appreciable fraction of genuine remnants.
A clean hot-water drill was used to gain access to Subglacial Lake Whillans (SLW) in late January 2013 as part of the Whillans Ice Stream Subglacial Access Research Drilling (WISSARD) project. Over 3 days, we deployed an array of scientific tools through the SLW borehole: a downhole camera, a conductivity–temperature–depth (CTD) probe, a Niskin water sampler, an in situ filtration unit, three different sediment corers, a geothermal probe and a geophysical sensor string. Our observations confirm the existence of a subglacial water reservoir whose presence was previously inferred from satellite altimetry and surface geophysics. Subglacial water is about two orders of magnitude less saline than sea water (0.37–0.41 psu vs 35 psu) and two orders of magnitude more saline than pure drill meltwater (<0.002 psu). It reaches a minimum temperature of –0.55~C, consistent with depression of the freezing point by 7.019 MPa of water pressure. Subglacial water was turbid and remained turbid following filtration through 0.45 µm filters. The recovered sediment cores, which sampled down to 0.8 m below the lake bottom, contained a macroscopically structureless diamicton with shear strength between 2 and 6 kPa. Our main operational recommendation for future subglacial access through water-filled boreholes is to supply enough heat to the top of the borehole to keep it from freezing.
This book presents a wide range of new research on many aspects of naval strategy in the early modern and modern periods. Among the themes covered are the problems of naval manpower, the nature of naval leadership and naval officers, intelligence, naval training and education, and strategic thinking and planning. The book is notable for giving extensive consideration to navies other than those ofBritain, its empire and the United States. It explores a number of fascinating subjects including how financial difficulties frustrated the attempts by Louis XIV's ministers to build a strong navy; how the absence of centralised power in the Dutch Republic had important consequences for Dutch naval power; how Hitler's relationship with his admirals severely affected German naval strategy during the Second World War; and many more besides. The book is a Festschrift in honour of John B. Hattendorf, for more than thirty years Ernest J. King Professor of Maritime History at the US Naval War College and an influential figure in naval affairs worldwide.
N.A.M. Rodger is Senior Research Fellow at All Souls College, Oxford.
J. Ross Dancy is Assistant Professor of Military History at Sam Houston State University.
Benjamin Darnell is a D.Phil. candidate at New College, Oxford.
Evan Wilson is Caird Senior Research Fellow at the National Maritime Museum, Greenwich.
Contributors: Tim Benbow, Peter John Brobst, Jaap R. Bruijn, Olivier Chaline, J. Ross Dancy, Benjamin Darnell, James Goldrick, Agustín Guimerá, Paul Kennedy, Keizo Kitagawa, Roger Knight, Andrew D. Lambert, George C. Peden, Carla Rahn Phillips, Werner Rahn, Paul M. Ramsey, Duncan Redford, N.A.M. Rodger, Jakob Seerup, Matthew S. Seligmann, Geoffrey Till, Evan Wilson
X-ray diffraction (XRD) has been routinely employed in the Earth sciences to characterize the crystallography of rocks and minerals. Routine characterization of samples too small for analysis by classic automated powder diffraction methods becomes challenging without access to single crystal or micro-diffraction equipment. Here, we show that a traditional Gandolfi camera lined with an image-plate (IP) as the detection medium can return a fully quantitative diffraction pattern from a sub-milligram single grain specimen in a simple and straightforward manner. Data pertaining to peak positions (d-spacings) were assessed using SRM640c Si powder, while intensity data were compared to the certified values for intensity standard SRM676a alumina powder. The refined unit-cell dimension of Si powder differed from the certified value of 5.4312 Å by no more than 0.0003 Å with a standard deviation (σ) of 0.0002 Å among the three experiments. For intensity, the σ and disparity from the certified values of three diffraction experiments on SRM676a were both <2%. The results of a comparative study of the crystallographic parameters determined for a naturally occurring garnet and clinopyroxene given through the refinement of their crystal structure by single-crystal XRD method are presented. These show through Rietveld refinement of X-ray data obtained by the Gandolfi–IP method outlined here that both accurate and precise XRD data can be produced in a timely and cost-effective manner using only an IP, Gandolfi camera, and software freely available on the internet.