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Background: Pediatric traumatic brain injury (TBI) affects about 475,000 children in the United States annually. Studies from the 1990s showed worse mortality in pediatric TBI patients not transferred to a pediatric trauma center (PTC), but did not examine mild pediatric TBI. Evidence-based guidelines used to identify children with clinically insignificant TBI who do not require head CT were developed by the Pediatric Emergency Care Applied Research Network (PECARN). However, which patients can be safely observed at a non-PTC is not directly addressed.Methods: A systematic review of the literature was conducted, focusing on management of pediatric TBI and transfer decisions from 1990 to 2020.Results: Pediatric TBI patients make up a great majority of preventable transfers and admissions, and comprise a significant portion of avoidable costs to the health care system. Majority of mild TBI patients admitted to a PTC following transfer do not require ICU care, surgical intervention, or additional imaging. Studies have shown that as high as 83% of mild pediatric TBI patients are discharged within 24 hrs.Conclusions: An evidence-based clinical practice algorithm was derived through synthesis of the data reviewed to guide transfer decision. The papers discussed in our systematic review largely concluded that transfer and admission was unnecessary and costly in pediatric patients with mild TBI who met the following criteria: blunt, no concern for NAT, low risk on PECARN assessment, or intermediate risk on PECARN with negative imaging or imaging with either isolated, nondisplaced skull fractures without ICH and/or EDH, or SDH <0.3 cm with no midline shift. [ABSTRACT FROM AUTHOR]

This paper investigates the possibility of the implementation of Genetic Programming (GP) algorithm on a publicly available COVID-19 data set, in order to obtain mathematical models which could be used for estimation of confirmed, deceased, and recovered cases and the estimation of epidemiology curve for specific countries, with a high number of cases, such as China, Italy, Spain, and USA and as well as on the global scale. The conducted investigation shows that the best mathematical models produced for estimating confirmed and deceased cases achieved R2 scores of 0.999, while the models developed for estimation of recovered cases achieved the R2 score of 0.998. The equations generated for confirmed, deceased, and recovered cases were combined in order to estimate the epidemiology curve of specific countries and on the global scale. The estimated epidemiology curve for each country obtained from these equations is almost identical to the real data contained within the data set [ABSTRACT FROM AUTHOR]

Background: Analyzing telemetry electrocardiogram (ECG) data over an extended period is often time-consuming because digital records are not widely available at hospitals. Investigating trends and patterns in the ECG data could lead to establishing predictors that would shorten response time to in-hospital cardiac arrest (I-HCA). This study was conducted to validate a novel method of digitizing paper ECG tracings from telemetry systems in order to facilitate the use of heart rate as a diagnostic feature prior to I-HCA. Methods: This multicenter study used telemetry to investigate full-disclosure ECG papers of 44 cardiovascular patients obtained within 1 hr of I-HCA with initial rhythms of pulseless electrical activity and asystole. Digital ECGs were available for seven of these patients. An algorithm to digitize the full-disclosure ECG papers was developed using the shortest path method. The heart rate was measured manually (averaging R-R intervals) for ECG papers and automatically for digitized and digital ECGs. Results: Significant correlations were found between manual and automated measurements of digitized ECGs (p < .001) and between digitized and digital ECGs (p < .001). Bland-Altman methods showed bias = .001 s, SD = .0276 s, lower and upper 95% limits of agreement for digitized and digital ECGs = .055 and -.053 s, and percentage error = 0.22%. Root mean square (rms), percentage rms difference, and signal to noise ratio values were in acceptable ranges. Conclusion: The digitization method was validated. Digitized ECG provides an efficient and accurate way of measuring heart rate over an extended period of time. [ABSTRACT FROM AUTHOR]

Some existing confidence interval methods and hypothesis testing methods in the analysis of a contingency table with incomplete observations in both margins entirely depend on an underlying assumption that the sampling distribution of the observed counts is a product of independent multinomial/binomial distributions for complete and incomplete counts. However, it can be shown that this independency assumption is incorrect and can result in unreliable conclusions because of the under-estimation of the uncertainty. Therefore, the first objective of this paper is to derive the valid joint sampling distribution of the observed counts in a contingency table with incomplete observations in both margins. The second objective is to provide a new framework for analyzing incomplete contingency tables based on the derived joint sampling distribution of the observed counts by developing a Fisher scoring algorithm to calculate maximum likelihood estimates of parameters of interest, the bootstrap confidence interval methods, and the bootstrap testing hypothesis methods. We compare the differences between the valid sampling distribution and the sampling distribution under the independency assumption. Simulation studies showed that average/expected confidence-interval widths of parameters based on the sampling distribution under the independency assumption are shorter than those based on the new sampling distribution, yielding unrealistic results. A real data set is analyzed to illustrate the application of the new sampling distribution for incomplete contingency tables and the analysis results again confirm the conclusions obtained from the simulation studies. [ABSTRACT FROM AUTHOR]

In recent decades, numerous methods have been developed for data mining of large drug safety databases, such as Food and Drug Administration's (FDA's) Adverse Event Reporting System, where data matrices are formed by drugs such as columns and adverse events as rows. Often, a large number of cells in these data matrices have zero cell counts and some of them are "true zeros" indicating that the drug-adverse event pairs cannot occur, and these zero counts are distinguished from the other zero counts that are modeled zero counts and simply indicate that the drug-adverse event pairs have not occurred yet or have not been reported yet. In this paper, a zero-inflated Poisson model based likelihood ratio test method is proposed to identify drug-adverse event pairs that have disproportionately high reporting rates, which are also called signals. The maximum likelihood estimates of the model parameters of zero-inflated Poisson model based likelihood ratio test are obtained using the expectation and maximization algorithm. The zero-inflated Poisson model based likelihood ratio test is also modified to handle the stratified analyses for binary and categorical covariates (e.g. gender and age) in the data. The proposed zero-inflated Poisson model based likelihood ratio test method is shown to asymptotically control the type I error and false discovery rate, and its finite sample performance for signal detection is evaluated through a simulation study. The simulation results show that the zero-inflated Poisson model based likelihood ratio test method performs similar to Poisson model based likelihood ratio test method when the estimated percentage of true zeros in the database is small. Both the zero-inflated Poisson model based likelihood ratio test and likelihood ratio test methods are applied to six selected drugs, from the 2006 to 2011 Adverse Event Reporting System database, with varying percentages of observed zero-count cells. [ABSTRACT FROM AUTHOR]

Methodologies for assessing human judgment in complex domains are important for the design of both displays that inform judgment and automated systems that suggest judgments. This paper uses the n-system lens model to evaluate the impact of displays on human judgment and to explicitly assess the similarity between human judgments and a set of potential judgment algorithms for use in automated systems. First, the need for and concepts underlying judgment analysis are outlined. Then the n-system lens model and its parameters are formally described. This model is then used to examine a previously conducted study of aircraft collision detection that had been analyzed using standard analysis of variance methods. Our analysis found the same main effects as did the earlier analysis. However, n-system lens model analysis was able to provide greater insight into the information relied upon for judgments and the impact of displays on judgment. Additionally, the analysis was able to identify attributes of human judgments that were--and were not--similar to judgments produced by automated systems. Potential applications of this research include automated aid design and operator training. [ABSTRACT FROM AUTHOR]