3DE Assessment of Pathological Spectrum of Aortic Prosthesis Dysfunction: Incremental Value over 2DE

2021 ◽  
pp. 203-213
Author(s):  
Joseph F. Maalouf
2016 ◽  
Vol 64 (S 01) ◽  
Author(s):  
A. Arsalan-Werner ◽  
M. Arsalan ◽  
W. Moll ◽  
M. Sauerbier ◽  
T. Walther

2005 ◽  
Vol 53 (S 01) ◽  
Author(s):  
P Landwehr ◽  
B Reichart ◽  
A Tiete ◽  
I Kaczmarek ◽  
M Müller ◽  
...  
Keyword(s):  

2018 ◽  
Vol 19 ◽  
pp. e50-e51
Author(s):  
L. Weltert ◽  
I. Chirichilli ◽  
S. D’Aleo ◽  
L. Guerrieri ◽  
A. Salica ◽  
...  
Keyword(s):  

2018 ◽  
Vol 38 (2) ◽  
pp. 27-55 ◽  
Author(s):  
Jean Bédard ◽  
Carl Brousseau ◽  
Ann Vanstraelen

SUMMARY Using a “natural experiment” provided by a change in Canadian auditing standards requiring an emphasis of matter paragraph in the auditor's report (GC-EOM) when the financial statements include a going concern uncertainty disclosure (GC-FS), this paper examines the incremental investor reaction to the auditor's report over the related GC-FS. Conditioning on the linguistic severity of the GC-FS (weak and severe), we first document a negative price response to severe but not to weak GC-FS before the regulatory change. This implies that investors react to financial statement disclosures and account for their degree of interpretability in the absence of a GC-EOM. When the uncertainty disclosure is accompanied by a GC-EOM, we find incremental negative abnormal returns and lower abnormal trading volume only for weak GC-FS. Collectively, these findings imply that an emphasis of matter paragraph in the auditor's report can have incremental value to investors. JEL Classifications: M42; G12; G14. Data Availability: Data used are available from public sources identified in the study.


2013 ◽  
Vol 20 (3) ◽  
pp. 265-270 ◽  
Author(s):  
Deborah B. Diercks ◽  
Bryn E. Mumma ◽  
W. Frank Peacock ◽  
Judd E. Hollander ◽  
Basmah Safdar ◽  
...  

Circulation ◽  
1968 ◽  
Vol 38 (3) ◽  
pp. 505-513 ◽  
Author(s):  
MARIO C. GARCIA ◽  
ALBERT M. CLARYSSE ◽  
CARL S. ALEXANDER ◽  
YOSHIO SAKO ◽  
WILLIAM R. SWAIM

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Maurizio Bartolucci ◽  
Matteo Benelli ◽  
Margherita Betti ◽  
Sara Bicchi ◽  
Luca Fedeli ◽  
...  

AbstractTriage is crucial for patient’s management and estimation of the required intensive care unit (ICU) beds is fundamental for health systems during the COVID-19 pandemic. We assessed whether chest computed tomography (CT) of COVID-19 pneumonia has an incremental role in predicting patient’s admission to ICU. We performed volumetric and texture analysis of the areas of the affected lung in CT of 115 outpatients with COVID-19 infection presenting to the emergency room with dyspnea and unresponsive hypoxyemia. Admission blood laboratory including lymphocyte count, serum lactate dehydrogenase, D-dimer and C-reactive protein and the ratio between the arterial partial pressure of oxygen and inspired oxygen were collected. By calculating the areas under the receiver-operating characteristic curves (AUC), we compared the performance of blood laboratory-arterial gas analyses features alone and combined with the CT features in two hybrid models (Hybrid radiological and Hybrid radiomics)for predicting ICU admission. Following a machine learning approach, 63 patients were allocated to the training and 52 to the validation set. Twenty-nine (25%) of patients were admitted to ICU. The Hybrid radiological model comprising the lung %consolidation performed significantly (p = 0.04) better in predicting ICU admission in the validation (AUC = 0.82; 95% confidence interval 0.73–0.97) set than the blood laboratory-arterial gas analyses features alone (AUC = 0.71; 95% confidence interval 0.56–0.86). A risk calculator for ICU admission was derived and is available at: https://github.com/cgplab/covidapp. The volume of the consolidated lung in CT of patients with COVID-19 pneumonia has a mild but significant incremental value in predicting ICU admission.


2020 ◽  
Vol 41 (9) ◽  
pp. 858-870
Author(s):  
Shwetal Uday Pawar ◽  
Sangeeta Hasmukh Ravat ◽  
Dattatraya Prakash Muzumdar ◽  
Shilpa Sushilkumar Sankhe ◽  
Akash Harakchand Chheda ◽  
...  

2017 ◽  
Vol 16 (3) ◽  
pp. 269-289
Author(s):  
Marc Bourreau ◽  
Bernard Caillaud ◽  
Romain de Nijs

Abstract In this paper we propose a model where consumer personal data have multidimensional characteristics, and are used by platforms to offer ad slots with better targeting possibilities to a market of differentiated advertisers through real-time auctions. A platform controls the amount of information about consumers that it discloses to advertisers, thereby affecting the dispersion of advertisers’ valuations for the slot. We first show by way of simulations that the amount of consumer-specific information that is optimally revealed to advertisers increases with the degree of competition on the advertising market and decreases with the cost of information disclosure for a monopolistic platform, competing platforms or a welfare-maximizing platform, provided the advertising market is not highly concentrated. Second, we exhibit different properties between the welfare-maximizing situation and the imperfectly competitive market situations with respect to how the incremental value of information varies: there are decreasing social returns to consumers’ data, while private returns may be increasing or decreasing locally.


Sign in / Sign up

Export Citation Format

Share Document