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To our knowledge, no previous meta-analysis has attempted to compare the efficacy of pharmacological, psychological and combined treatments for the three main anxiety disorders (panic disorder, generalized anxiety disorder and social phobia). Pre–post and treated versus control effect sizes (ES) were calculated for all evaluable randomized-controlled studies (n=234), involving 37 333 patients. Medications were associated with a significantly higher average pre–post ES Cohen’s d=2.02 (1.90–2.15); 28 051 patients than psychotherapies 1.22 (1.14–1.30); 6992 patients; P<0.0001. ES were 2.25 for serotonin–noradrenaline reuptake inhibitors (n=23 study arms), 2.15 for benzodiazepines (n=42), 2.09 for selective serotonin reuptake inhibitors (n=62) and 1.83 for tricyclic antidepressants (n=15). ES for psychotherapies were mindfulness therapies, 1.56 (n=4); relaxation, 1.36 (n=17); individual cognitive behaviouralexposure therapy (CBT), 1.30 (n=93); group CBT, 1.22 (n=18); psychodynamic therapy 1.17 (n=5); therapies without face-to-face contact (e.g. Internet therapies), 1.11 (n=34); eye movement desensitization reprocessing, 1.03 (n=3); and interpersonal therapy 0.78 (n=4). The ES was 2.12 (n=16) for CBTdrug combinations. Exercise had an ES of 1.23 (n=3). For control groups, ES were 1.29 for placebo pills (n=111), 0.83 for psychological placebos (n=16) and 0.20 for waitlists (n=50). In direct comparisons with control groups, all investigated drugs, except for citalopram, opipramol and moclobemide, were significantly more effective than placebo. Individual CBT was more effective than waiting list, psychological placebo and pill placebo. When looking at the average pre–post ES, medications were more effective than psychotherapies. Pre–post ES for psychotherapies did not differ from pill placebos; this finding cannot be explained by heterogeneity, publication bias or allegiance effects. However, the decision on whether to choose psychotherapy, medications or a combination of the two should be left to the patient as drugs may have side effects, interactions and contraindications.

The search for gravitational-wave signals in detector data is often hampered by the fact that many data analysis methods are based on the theory of stationary Gaussian noise, while actual measurement data frequently exhibit clear departures from these assumptions. Deriving methods from models more closely reflecting the data's properties promises to yield more sensitive procedures. The commonly used matched filter is such a detection method that may be derived via a Gaussian model. In this paper we propose a generalized matched-filtering technique based on a Student-t distribution that is able to account for heavier-tailed noise and is robust against outliers in the data. On the technical side, it generalizes the matched filter's least-squares method to an iterative, or adaptive, variation. In a simplified Monte Carlo study we show that when applied to simulated signals buried in actual interferometer noise it leads to a higher detection rate than the usual ("Gaussian") matched filter. [ABSTRACT FROM AUTHOR]

In this paper we present a description of a Bayesian analysis framework for use with interferometric gravitational radiation data in search of binary neutron star inspiral signals. Five parameters are investigated, and the information extracted from the data is illustrated and quantified. The posterior integration is carried out using Markov chain Monte Carlo (MCMC) methods. Implementation details include the use of importance resampling for improved convergence and informative priors reflecting the conditions expected for realistic measurements. An example is presented from an application using realistic, albeit fictitious, data. We expect that these parameter estimation techniques will prove useful at the end of a binary inspiral detection pipeline for interferometric detectors like LIGO or Virgo.

The usefulness of measures of neutrophil gelatinase-associated lipocalin (NGAL) in urine or plasma obtained on clinical laboratory platforms for predicting acute kidney injury (AKI) and AKI requiring dialysis (AKI-D) has not been fully evaluated. We sought to quantitatively summarize published data to evaluate the value of urinary and plasma NGAL for kidney risk prediction.