1,721,868 research outputs found
Problems of using grouped DMUs for efficiency measurement: Monte Carlo experiments, empirical dimension, and a correction procedure
No full textThis paper explores the consequences for parametric and non-parametric efficiency levels and rankings when using grouped instead of individual Decision Making Units (DMU). The bias results due to the differences of the grouped DMUs frontier compared to the individual DMUs frontier. Monte Carlo experimentation is used to evaluate the empirical dimension on the estimated efficiency levels and rankings. These results are illustrated with an empirical example using a sample of German farms. The bias in ranking is found to be substantial. Finally, a correction procedure is developed to improve the results when only grouped data are available
Problems of using grouped DMUs for efficiency measurement: Monte Carlo experiments, empirical dimension, and a correction procedure
No full textThis paper explores the consequences for parametric and non-parametric efficiency levels and rankings when using grouped instead of individual Decision Making Units (DMU). The bias results due to the differences of the grouped DMUs frontier compared to the individual DMUs frontier. Monte Carlo experimentation is used to evaluate the empirical dimension on the estimated efficiency levels and rankings. These results are illustrated with an empirical example using a sample of German farms. The bias in ranking is found to be substantial. Finally, a correction procedure is developed to improve the results when only grouped data are available
A cardiologist’s guide to machine learning in cardiovascular disease prognosis prediction
No full textAbstract A modern-day physician is faced with a vast abundance of clinical and scientific data, by far surpassing the capabilities of the human mind. Until the last decade, advances in data availability have not been accompanied by analytical approaches. The advent of machine learning (ML) algorithms might improve the interpretation of complex data and should help to translate the near endless amount of data into clinical decision-making. ML has become part of our everyday practice and might even further change modern-day medicine. It is important to acknowledge the role of ML in prognosis prediction of cardiovascular disease. The present review aims on preparing the modern physician and researcher for the challenges that ML might bring, explaining basic concepts but also caveats that might arise when using these methods. Further, a brief overview of current established classical and emerging concepts of ML disease prediction in the fields of omics, imaging and basic science is presented
Questions and answers on antithrombotic therapy and revascularization strategies in non-ST-elevation acute coronary syndrome (NSTE-ACS): a companion document of the 2020 ESC Guidelines for the management of acute coronary syndromes in patients presenting without persistent ST-segment elevation
No full textnot availabl
Economic implications of intra-aortic balloon support for myocardial infarction with cardiogenic shock: an analysis from the IABP-SHOCK II-trial
No full textThe Intra-aortic Balloon Pump in Cardiogenic Shock II (IABP-SHOCK II) trial has demonstrated the safety of intra-aortic balloon (IABP) support in patients with acute myocardial infarction (AMI) complicated by cardiogenic shock, but no beneficial effect on mortality. Currently, intra-aortic balloon pumping is still the most widely used support device. However, little is known about the economic implications associated with this device. Data of 600 patients included in the IABP-SHOCK II trial (registered at ClinicalTrials.gov, NCT00491036) with follow-up at 30 days, 6 and 12 months were subjected to an economic analysis. Patients with cardiogenic shock complicating AMI were randomly assigned to IABP additionally to optimal medical therapy (OMT; n = 301) or OMT alone (n = 299) before early revascularization. Costs were calculated from the perspective of a German healthcare payer. Cost-effectiveness and cost-utility analyses were performed using quality-adjusted life years (QALY) and reduction in New York Heart Association (NYHA) and Canadian Cardiac Society (CCS) class as effectiveness measures. There was a statistically significant difference in overall costs between the IABP (33,155 +/- A 14,593 a,not sign) and the control group (32,538 +/- A 14,031 a,not sign, p < 0.00001). This was predominantly attributed to the IABP costs in the IABP (760 +/- A 174 a,not sign) versus control group (64 +/- A 218 a,not sign, p < 0.0001) whilst the intensive care unit costs did not differ between the groups (29,177 +/- A 12,013 a,not sign and 29,401 +/- A 12,063 a,not sign, p = 0.82). There was no significant difference in QALY or NYHA and CCS reduction, respectively (p = n.s.). IABP support is associated with higher healthcare costs as compared to conservative treatment regimens. Clinically, IABP support cannot generally be recommended in AMI complicated by cardiogenic shock in the absence of a mortality benefit. However, economically considering the relatively little contribution to overall costs generated by IABP therapy it may still be considered if clinical scenarios with an IABP-induced benefit may be identified in the future
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe 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
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe 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
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