305,296 research outputs found
Um dicionário alemao-português de verbos com prefixos
No full textUm dicionário alemao-português de verbos com prefixos : objectivos e consideraçoes metodológicas. - In: Lexicografías iberorománicas / M. Teresa Fuentes Morán ... (eds.). - Frankfurt am Main u.a. : Vervuert u.a., 1998. - S. 185-199. - (Aspectos de lingüística aplicada ; 1
Um dicionário alemao-português de verbos com prefixos
No full textUm dicionário alemao-português de verbos com prefixos : objectivos e consideraçoes metodológicas. - In: Lexicografías iberorománicas / M. Teresa Fuentes Morán ... (eds.). - Frankfurt am Main u.a. : Vervuert u.a., 1998. - S. 185-199. - (Aspectos de lingüística aplicada ; 1
Cycling of tumor necrosis factor inhibitors switching to different mechanism of action therapy in rheumatoid arthritis patients with inadequate response to tumor necrosis factor inhibitors: a Bayesian network meta-analysis
No full textIntroduction: For patients with rheumatoid arthritis (RA) with an inadequate response to tumor necrosis factor inhibitors (TNFi), main options include cycling onto a different TNFi or switching to a biologic/targeted synthetic disease-modifying antirheumatic drug with a different mechanism of action (MOA). This network meta-analysis (NMA) assessed comparative clinical efficacy of cycling versus switching. Methods: We conducted a literature search in MEDLINE, Embase, and Cochrane Library. Outcomes included proportion of patients with 20%, 50%, or 70% response to American College of Rheumatology criteria (ACR20/ACR50/ACR70 response), Disease Activity Score in 28 joints (DAS28) score below 2.6 or between 2.6 and 3.2, mean change in DAS28 score, mean reduction in and proportion of patients achieving a clinically meaningful reduction (⩾0.22) in Health Assessment Questionnaire score, number of serious adverse events (AEs), and withdrawals for any reason/due to AEs/lack of treatment efficacy. To account for the wide range of study populations and designs, we developed three models to conduct the NMA: fixed-effect, random-effects, and hierarchical Bayesian. PROSPERO ID: CRD42019122993. Results: We identified nine randomized controlled trials and 16 observational studies. The fixed-effect model suggested a 0.99 probability that switch was the better strategy for increasing odds of a clinically meaningful improvement in ACR50 [odds ratio (OR): 1.35 (95% credible interval (CI): 0.96–1.81)]. The fixed-effect model also suggested that switch was associated with lower rates of withdrawal for any reasons [OR: 0.53 (95% CI: 0.40–0.68)]. The random-effects and hierarchical Bayesian models suggested additional uncertainty as they considered more variability than the fixed-effect model. Discussion: Results suggest that switching to a drug with a different MOA is more effective and associated with lower rates of withdrawal than cycling to a different TNFi after failure of first-line TNFi. Further trials that directly compare cycling with switching are warranted to better assess comparative efficacy. Plain language summary Assessment of the effectiveness of different drug treatment strategies in patients with rheumatoid arthritis: an analysis of the published literature Rheumatoid arthritis (RA) is a chronic disease in which inflammation affects joints along with the entire body; this may cause significant pain, joint damage, physical disability, a decreased quality of life, and an increased risk of death. Tumor necrosis factor inhibitors (TNFis) are a common choice as first-line drugs to treat RA. Although they are effective in many patients, therapy with a TNFi is not successful within the first year of treatment in approximately one-third of patients due to either a lack of efficacy or safety issues. When TNFi therapy is unsuccessful, the options are to “cycle” to another TNFi or to “switch” to another drug with a different mechanism of action (MOA). Further studies are needed to help doctors decide the best treatment strategy for their patients when treatment with an initial TNFi fails. This study analyzed 25 published studies in which patients were either “cycled” to another TNFi or “switched” to a drug with a different MOA after unsuccessful treatment with an initial TNFi. The results showed that “switching” to a drug with a different MOA was a better treatment strategy than “cycling” to another TNFi; “switching” increased the chance of clinically meaningful improvement in disease status and lowered the chance of having to stop treatment for any reason
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
Dispelling the Myths Behind First-author Citation Counts
No full textWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
Author, publisher and bookseller : a tripartite synergy in Nigerian book industry
No full textThis work is about the roles of Author, Publisher and Bookseller in Book development in
Nigeria. The paper started by delving into the history of Book Publishing in Nigeria after
which it proceeded by defining who an author, a publisher, and a bookseller is and
expatiated on the indispensable roles of these key actors in Nigerian Book Industry and in
the emerging Information Society. Furthermore, the various constraints to book
development were identified while the paper advised on how the Book Industry can be
further promoted in Nigeria. However, the paper concluded and made recommendations
on how the Book sector can help in enhancing scholarship in the country
[Report to Chief J. E. Curry, by an unknown author #2]
No full textReport to Chief J. E. Curry, by an unknown author. The report contains a list of officers who gave depositions to the United States Attorney
[Report to Chief J. E. Curry, by an unknown author #1]
No full textReport to Chief J. E. Curry, by an unknown author. The report contains a list of officers who gave depositions to the United States Attorney
Mining e-mail content for author identification forensics
No full textWe describe an investigation into e-mail content mining for author identification, or authorship attribution, for the purpose of forensic investigation. We focus our discussion on the ability to discriminate between authors for the case of both aggregated e-mail topics as well as across different email topics. An extended set of e-mail document features including structural characteristics and linguistic patterns were derived and, together with a Support Vector Machine learning algorithm, were used for mining the e-mail content. Experiments using a number of e-mail documents generated by different authors on a set of topics gave promising results for both aggregated and multi-topic author categorisation
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