Psychosocial interventions had medium-size effects on both pain severity and interference. These robust findings support the systematic implementation of quality-controlled psychosocial interventions as part of a multimodal approach to the management of pain in patients with cancer.

Our findings suggest that psychological and pharmacologic approaches can be targeted productively toward cancer patients with elevated depressive symptoms. Research is needed to maximize effectiveness, accessibility, and integration into clinical care of interventions for depressed cancer patients.

The goal of pharmacovigilance is to detect, monitor, characterize and prevent adverse drug events (ADEs) with pharmaceutical products. This article is a comprehensive structured review of recent advances in applying natural language processing (NLP) to electronic health record (EHR) narratives for pharmacovigilance. We review methods of varying complexity and problem focus, summarize the current state-of-the-art in methodology advancement, discuss limitations and point out several promising future directions. The ability to accurately capture both semantic and syntactic structures in clinical narratives becomes increasingly critical to enable efficient and accurate ADE detection. Significant progress has been made in algorithm development and resource construction since 2000. Since 2012, statistical analysis and machine learning methods have gained traction in automation of ADE mining from EHR narratives. Current state-of-the-art methods for NLP-based ADE detection from EHRs show promise regarding their integration into production pharmacovigilance systems. In addition, integrating multifaceted, heterogeneous data sources has shown promise in improving ADE detection and has become increasingly adopted. On the other hand, challenges and opportunities remain across the frontier of NLP application to EHR-based pharmacovigilance, including proper characterization of ADE context, differentiation between off- and on-label drug-use ADEs, recognition of the importance of polypharmacy-induced ADEs, better integration of heterogeneous data sources, creation of shared corpora, and organization of shared-task challenges to advance the state-of-the-art.

Control conditions are the primary methodology used to reduce threats to internal validity in randomized controlled trials (RCTs). This meta-analysis examined the effects of control arm design and implementation on outcomes in RCTs examining psychological treatments for depression. A search of MEDLINE, PsycINFO, and EMBASE identified all RCTs evaluating psychological treatments for depression published through June 2009. Data were analyzed using mixedeffects models. One hundred twenty-five trials were identified yielding 188 comparisons. Outcomes varied significantly depending control condition design (p<0.0001). Significantly smaller effect sizes were seen when control arms used manualization (p=0.006), therapist training (p=0.002), therapist supervision (p=0.009), and treatment fidelity monitoring (p=0.003). There were no significant effects for differences in therapist experience, level of expertise in the treatment delivered, or nesting vs. crossing therapists in treatment arms. These findings demonstrate the substantial effect that decisions regarding control arm definition and implementation can have on RCT outcomes. KeywordsMeta-analysis, Depression, Control conditions, Randomized controlled trial design, Methodology Over the past half century, evidence has accumulated to support a number of psychological and behavioral interventions for mental health and medical conditions [6]. The backbone of treatment outcome research is the randomized controlled trial (RCT), a planned experiment designed to test the efficacy or effectiveness of an intervention. Although many aspects of RCT methodology have received considerable attention [2], until recently, surprisingly little attention has been paid to how to select and implement control conditions. The aim of this paper is to examine the effects of the design and implementation of control conditions on RCT outcomes for the treatment of depression using meta-analysis. These results will be interpreted in light of recent efforts to formulate a framework to support decisions regarding the selection, design, and implementation of control conditions [20].RCTs can vary in their aim, from explanatory trials evaluating efficacy or effectiveness under ideal conditions, to more pragmatic trials that evaluate the intervention under conditions found in clinical settings [13,28]. In either case, the experimental treatment is always determined relative to a control condition. Consequently, what an RCT reveals about the effectiveness of the experimental treatment inherently depends as much on the control condition as on the experimental treatment. One of the principal reasons for using a control condition is to eliminate alternative causal explanations. In statistical terms, the purpose of a control condition is to filter out the variance due to factors that are not specific to the experimental intervention, leaving only the variance due specifically

There is uncertainty whether current strategies for providing CVD risk scores affect CVD events. Providing CVD risk scores may slightly reduce CVD risk factor levels and may increase preventive medication prescribing in higher-risk people without evidence of harm. There were multiple study limitations in the identified studies and substantial heterogeneity in the interventions, outcomes, and analyses, so readers should interpret results with caution. New models for implementing and evaluating CVD risk scores in adequately powered studies are needed to define the role of applying CVD risk scores in primary CVD prevention.