1,723,560 research outputs found
Assessing the Reliability of Google Scholar in Predicting Scopus Citation Metrics
No full textThis study analyzed the relationship between bibliometric metrics—specifically the H-index and citation counts—obtained from Google Scholar and Scopus, two widely used databases for assessing research impact. The analysis was based on data from 30 academics affiliated with the University of Verona. Strong correlations within each database were observed, demonstrating that both consistently capture similar patterns of scientific impact. The high degree of concordance between Google Scholar and Scopus metrics also indicates that they provide comparable rankings and relative measures of academic performance, despite differences in absolute values. On average, citation counts from Scopus were 33.8% lower than those from Google Scholar, while H-index values from Scopus were 16.8% lower. These findings highlight the critical importance of database selection in research evaluations, advocating the use of complementary metrics derived from multiple databases to achieve a balanced and comprehensive assessment of scientific impact, while also accounting for the unique strengths and limitations of each bibliometric source
The impact factor for evaluating scientists: the good, the bad and the ugly.
No full textThe impact factor for evaluating scientists: the good, the bad and the ugly
Machine learning in laboratory diagnostics: valuable resources or a big hoax?
No full textMachine learning in laboratory diagnostics: valuable resources or a big hoax
Pistorious at the Olympics: the saga continues.
Full text linkPistorious at the Olympics: the saga continues
The mystifying nomenclature of cardiac troponin immunoassays.
No full textThe laboratory assessment of cardiospecific troponins(s) represents the cornerstone for the diagnosis of acute coronary syndrome (ACS). Although troponin immunoassays are classified according to either analytical imprecision or percentage of measurable values in a presumably healthy population, it is rather clear that the nomenclature of commercial methods according to these systems of classification carries several drawbacks. The leading problems in classification according to imprecision are represented by the arbitrarity of optimal imprecision threshold, the uncertain correspondence between analytical performance and clinical outcomes and the improper use of terms, which has also been magnified by the lack of specific focus on this topic by regulating bodies such as the US Food and Drug Administration (FDA) and the European Union. Additional issues emerging from classification according to percentage of measurable values include the characterization of healthy population, the variation of values according to age, gender and race, as well as the influence of comorbidities. Considering that what really matters from a clinical standpoint is the clinical performance of the assay rather than the claimed analytical characteristics, it seems reasonable at this point in time to introduce a paradigm shift and gradually abandon the former analytical classification in favour of a different approach, preferable based on clinical outcomes
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