scholarly journals ABCDE approach to victims by lifeguards: How do they manage a critical patient? A cross sectional simulation study

PLoS ONE ◽  
2019 ◽  
Vol 14 (4) ◽  
pp. e0212080 ◽  
Author(s):  
Felipe Fernández-Méndez ◽  
Martín Otero-Agra ◽  
Cristian Abelairas-Gómez ◽  
Nieves María Sáez-Gallego ◽  
Antonio Rodríguez-Núñez ◽  
...  
2018 ◽  
Vol 53 (21) ◽  
pp. 15165-15180 ◽  
Author(s):  
Eisuke Miyoshi ◽  
Tomohiro Takaki ◽  
Munekazu Ohno ◽  
Yasushi Shibuta ◽  
Shinji Sakane ◽  
...  

2005 ◽  
Vol 26 (4) ◽  
pp. 362-368 ◽  
Author(s):  
Jose Rossello-Urgell ◽  
Alicia Rodriguez-Pla

AbstractObjective:To date, it has not been adequately proven whether the published formulas used to obtain incidence from the prevalence of nosocomial infections provide a good estimate of real incidence. With the hypothesis that within the hospital setting prevalence may be lower than incidence, the aim of this study was to analyze the behavior of point prevalence as it relates to cumulative incidence and duration of infection.Design:Hospital simulation study.Methods:By randomly selecting a sample of infected patients within a specific range of cumulative incidences and infection durations, we constructed a simulated hospital population, allowing us to estimate daily point prevalences and their maximum and minimum values. The association between the different components of stay and cumulative incidence was evaluated to obtain a more accurate estimate of incidence.Results:Prevalence can be lower than, equal to, or higher than the corresponding incidence. For all incidence levels, prevalence was increasing with duration. Between 14 and 20 days of infection duration, prevalence was consistently lower than incidence. Prevalence duration of infection was approximately half the time of the total duration.Conclusions:The existing formulas relating incidence and prevalence can frequently be inadequate. Until a validated system for converting prevalence into incidence is available, we do not believe their use is appropriate.


2018 ◽  
Vol 44 ◽  
pp. 404-406 ◽  
Author(s):  
Mikkel Brabrand ◽  
Peter Hallas ◽  
Lars Folkestad ◽  
Cecilie Hovitz Lautrup-Larsen ◽  
Jacob Broder Brodersen

Author(s):  
Jonas M. B. Haslbeck

AbstractStatistical network models such as the Gaussian Graphical Model and the Ising model have become popular tools to analyze multivariate psychological datasets. In many applications, the goal is to compare such network models across groups. In this paper, I introduce a method to estimate group differences in network models that is based on moderation analysis. This method is attractive because it allows one to make comparisons across more than two groups for all parameters within a single model and because it is implemented for all commonly used cross-sectional network models. Next to introducing the method, I evaluate the performance of the proposed method and existing approaches in a simulation study. Finally, I provide a fully reproducible tutorial on how to use the proposed method to compare a network model across three groups using the R-package mgm.


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