130,743 research outputs found
Accurate theoretical study of the excited states of boron and aluminum carbides, BC, AlC. 2
No full textContinuing our study on the electronic structure of the carbides BC and AlC (Tzeli, D.; Mavridis, A. J. Phys. Chem. A 2001, 105, 1175), we have investigated the electronic structure of 29 and 30 excited states of the BC and AlC molecules, respectively, by ab initio quantum mechanical multireference methods and quantitative basis sets. For both diatomic species we report complete potential energy curves, total energies, interatomic distances, dissociation energies, dipole moments, Mulliken charges, energy gaps, and usual spectroscopic constants. Our results are, in general, in good to very good agreement with the existing experimental values
Comparative Superiority of ACE Inhibitors over Angiotensin Receptor Blockers for People with CKD: Does It Matter?
No full textA fully Bayesian application of the Copas selection model for publication bias extended to network meta-analysis.
No full textThe Copas parametric model is aimed at exploring the potential impact of publication bias via sensitivity analysis, by making assumptions regarding the probability of publication of individual studies related to the standard error of their effect sizes. Reviewers often have prior assumptions about the extent of selection in the set of studies included in a meta-analysis. However, a Bayesian implementation of the Copas model has not been studied yet. We aim to present a Bayesian selection model for publication bias and to extend it to the case of network meta-analysis where each treatment is compared either with placebo or with a reference treatment creating a star-shaped network. We take advantage of the greater flexibility offered in the Bayesian context to incorporate in the model prior information on the extent and strength of selection. To derive prior distributions, we use both external data and an elicitation process of expert opinion
MeSH term explosion and author rank improve expert recommendations
No full textInformation overload is an often-cited phenomenon that reduces the productivity, efficiency and efficacy of scientists. One challenge for scientists is to find appropriate collaborators in their research. The literature describes various solutions to the problem of expertise location, but most current approaches do not appear to be very suitable for expert recommendations in biomedical research. In this study, we present the development and initial evaluation of a vector space model-based algorithm to calculate researcher similarity using four inputs: 1) MeSH terms of publications; 2) MeSH terms and author rank; 3) exploded MeSH terms; and 4) exploded MeSH terms and author rank. We developed and evaluated the algorithm using a data set of 17,525 authors and their 22,542 papers. On average, our algorithms correctly predicted 2.5 of the top 5/10 coauthors of individual scientists. Exploded MeSH and author rank outperformed all other algorithms in accuracy, followed closely by MeSH and author rank. Our results show that the accuracy of MeSH term-based matching can be enhanced with other metadata such as author rank
Going Beyond Counting First Authors in Author Co-citation Analysis
No full textThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Network meta‐analysis models to account for variability in treatment definitions: application to dose effects
No full textFor a network meta-analysis, an interlinked network of nodes representing competing treatments is needed. It is often challenging to define the nodes as these typically refer to similar but rarely identical interventions. The objectives of this paper are as follows: (i) to present a series of network meta-analysis models that account for variation in the definition of the nodes and (ii) to exemplify the models where variation in the treatment definitions relates to the dose. Starting from the model that assumes each node has a 'fixed' definition, we gradually introduce terms to explain variability by assuming that each node has several subnodes that relate to different doses. The effects of subnodes are considered monotonic, linked with a 'random walk', random but exchangeable, or have a linear pattern around the treatment mean effect. Each model can be combined with different assumptions for the consistency of effects and might impact on the ranking of the treatments. Goodness of fit, heterogeneity and inconsistency were assessed. The models are illustrated in a star network for the effectiveness of fluoride toothpaste and in a full network comparing agents for multiple sclerosis. The fit and parsimony measures indicate that in the fluoride network the impact of the dose subnodes is important whereas in the multiple sclerosis network the model without subnodes is the most appropriate. The proposed approach can be a useful exploratory tool to explain sources of heterogeneity and inconsistency when there is doubt whether similar interventions should be grouped under the same node. © 2012 John Wiley & Sons, Ltd
Comparative Effectiveness of Calcimimetic Agents for Secondary Hyperparathyroidism in Adults: A Systematic Review and Network Meta-analysis
No full textRationale & Objective: Comparative benefits and harms of calcimimetic agents used for the treatment of secondary hyperparathyroidism have not been well characterized. We sought to compare the effectiveness of 3 calcimimetic agents using published data. Study Design: Systematic review of randomized controlled trials and network meta-analysis. Setting & Study Population: Adults with chronic kidney disease enrolled in a clinical trial of a calcimetic agent. Search Strategy & Sources: MEDLINE, EMBASE, CENTRAL (from February 7, 2013, to November 21, 2019), and a published meta-analysis. Data Extraction: Two reviewers independently extracted the study data, assessed risk of bias, and rated evidence certainty using Grading of Recommendations Assessment, Development and Evaluation (GRADE) criteria. Analytical Approach: Frequentist network meta-analysis was conducted. The primary review outcomes were achievement of a target reduction in serum parathyroid hormone (PTH) levels and hypocalcemia. Additional outcomes were nausea, vomiting, serious adverse events, all-cause mortality, cardiovascular mortality, heart failure, and fracture. Results: 36 trials (11,247 participants) were included. All except 4 trials involved dialysis patients. Median follow-up was 26 weeks (range, 1 week to 21.2 months). Compared with placebo, calcimimetic agents had higher odds of achieving target PTH levels with high or moderate certainty. Etelcalcetide had the highest odds of achieving a PTH target compared with evocalcet (OR, 4.93; 95% CI, 1.33-18.2) and cinacalcet (OR, 2.78; 95% CI, 1.19-6.67). Etelcalcetide appeared to cause more hypocalcemia than cinacalcet and evocalcet. Cinacalcet and to a lesser extent etelcalcetide appeared to cause more nausea than placebo. Differences in risk for mortality, cardiovascular end points, or fractures across calcimimetic agents could not be discerned with sufficient certainty. Limitations: Lack of longer-term data; heterogeneous end point definitions. Conclusions: Evidence of the benefits of calcimimetic therapy is limited to short-term assessment of a putative surrogate outcome (serum PTH). Although etelcalcetide was associated with the largest reduction in PTH levels, side-effect profiles differed across the 3 calcimimetic agents, making it not possible to identify 1 preferred agent
"Closing the R&D Gap, Evaluating the Sources of R&D Spending"
No full textBoth spending and tax policies have been implemented in the United States with the goal of stimulating private sector research and development (R&D). Karier questions whether current R&D policy, especially the research and experimentation tax credit, can contribute to closing the gap between nondefense expenditures on R&D in the United States and such expenditures in other countries, such as Japan and Germany. He also explores possible changes to our current R&D policy to make it more effective.
Joint synthesis of multiple correlated outcomes in networks of interventions.
No full textMultiple outcomes multivariate meta-analysis (MOMA) is gaining in popularity as a tool for jointly synthesizing evidence coming from studies that report effect estimates for multiple correlated outcomes. Models for MOMA are available for the case of the pairwise meta-analysis of two treatments for multiple outcomes. Network meta-analysis (NMA) can be used for handling studies that compare more than two treatments; however, there is currently little guidance on how to perform an MOMA for the case of a network of interventions with multiple outcomes. The aim of this paper is to address this issue by proposing two models for synthesizing evidence from multi-arm studies reporting on multiple correlated outcomes for networks of competing treatments. Our models can handle continuous, binary, time-to-event or mixed outcomes, with or without availability of within-study correlations. They are set in a Bayesian framework to allow flexibility in fitting and assigning prior distributions to the parameters of interest while fully accounting for parameter uncertainty. As an illustrative example, we use a network of interventions for acute mania, which contains multi-arm studies reporting on two correlated binary outcomes: response rate and dropout rate. Both multiple-outcomes NMA models produce narrower confidence intervals compared with independent, univariate network meta-analyses for each outcome and have an impact on the relative ranking of the treatments
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