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COVID-19 Spatiotemporal Hotspots and Prediction Based on Wavelet and Neural Network
Published in Abbas Rajabifard, Greg Foliente, Daniel Paez, COVID-19 Pandemic, Geospatial Information, and Community Resilience, 2021
Neda Kaffash Charandabi, Amir Gholami
Network Common Data Form (NetCDF) is a file format to store multi-dimensional scientific data such as temperature, humidity, disease, and crime. The NetCDF cube is generated using the COVID-19 x, y, and time data as x, y, and z axes. It summarizes a collection of points into a NetCDF by aggregating them into space-time bins. The Mann-Kendall p-values and z-scores show the statistical significance of the trend in a hot spot (spatial clusters of high values) or cold spot (spatial clusters of low values) at a location. A positive or negative z-score indicates an upward or downward trend respectively [9, 27]. Then, the pattern in the spatiotemporal data was identified with Getis-Ord Gi* statistic based on neighborhood distance and neighborhood time step. The Getis-Ord Gi* statistic is calculated for each bin as follows [28, 29]:
Diagnosis
Published in Peter V. Giannoudis, Thomas A. Einhorn, Surgical and Medical Treatment of Osteoporosis, 2020
The Z-score measures the patient's bone density in comparison to that in people of similar age, sex, and ethnicity. The Z-score is most commonly used in cases of severe osteoporosis to guide further testing for coexisting conditions that may contribute to osteoporosis. Z-scores lower than –2.0 are defined as below the expected range for age.
Norms and Scores
Published in Lucy Jane Miller, Developing Norm-Referenced Standardized Tests, 2020
A z-score is defined as a standard score with a mean of 0 and a standard deviation of 1. A raw score is converted to a z-score using the equation: In the equation, X = the raw score, X = the mean, and SD = the standard deviation.
Saccadic Eye Movements in Patients with Mild Cognitive Impairment: A Longitudinal Study
Published in Journal of Motor Behavior, 2023
Müge Akkoyun, Koray Koçoğlu, Hatice Eraslan Boz, Pembe Keskinoğlu, Gülden Akdal
All data of the study were analyzed using the Statistical Package for Social Sciences software (SPSS 25.0). Significance was accepted at the p < 0.05 level for all tests. Normality distribution was verified by the Kurtosis and Skewness Coefficients (z-score <1.96) and the Shapiro-Wilk Test. Kruskal-Wallis H Test and Mann-Whitney U Test were used for the analysis of data that did not exhibit normal distribution. Chi-square (X2) test was used for categorical variables. The standard neuropsychological composite z-score was used for five cognitive domains to compare the cognitive functions of HCs and subtypes of MCI. Composite z-score transformation for each cognitive domain was calculated using neuropsychological tests. Composite z-score is a statistical method commonly used to combine the results of multiple tests or assessments into a single measure. A z-score describes the relationship of a particular value to the mean of a group of values. Z-score indicates the number of standard deviations that the value is away from the mean of the group. Z-scores may have positive or negative values. Positive values determine the score is above the mean and a negative score indicates it is below the mean (Andrade, 2021). Kruskal-Wallis H test with Dunn’s post-hoc test was used to analyze the pairwise comparisons of all baseline and follow-up assessments. Also, Wilcoxon signed-rank test was used for changes in baseline and follow-up cognitive evaluations.
Training and competition injury epidemiology in professional basketball players: a prospective observational study
Published in The Physician and Sportsmedicine, 2023
Victor Moreno-Pérez, Javier Ruiz, Jairo Vazquez-Guerrero, Gil Rodas, Juan Del Coso
The data from the injury reports were meticulously extracted from the reports of the medical staff by one author (JR) and they were transferred to a spreadsheet (Excel 2016, Microsoft Office, WA, USA) with the definitions and grouping suggested by the IOC consensus statement [17]. First, the mean and standard deviation (SD) for the participants’ characteristics were obtained for the years under scrutiny. Then, the number of injuries was subsequently calculated for each season and data were cross-tabulated with exposure times in training and competition to calculate injury incidence per season and as a whole for the six seasons. Afterward, injuries were classified using the categories mentioned above and distributions were calculated as a function of the total injuries per year and as a whole for the six seasons. The differences in distribution of injuries during training and competition were tested with crosstabs with the Chi-square tests, including adjusted standardized residuals. Briefly, it was considered that the proportion of injuries during training vs. competition had a statistically different distribution from the expected value when the distribution of injuries within the variable under investigation was > or < the critical Z-score value (i.e. 1.96). As the Z-score is a measure of standard deviation, the variables that surpassed the above-mentioned threshold contained data that were 1.96 standard deviations higher and lower than the mean value. The significance level was set at P < 0.05.
Assessment methods for inter-laboratory comparisons of the dicentric assay
Published in International Journal of Radiation Biology, 2023
Jorge Ernesto González Mesa, Bret Holladay, Manuel Higueras, Marina Di Giorgio, Joan Francesc Barquinero
The real examples of ILCs selected are the 0.7 Gy data published by Lloyd et al. (1987), and the 0 Gy data published by Gregoire et al. (2021). These examples represent a challenge for the assessment of an inter-laboratory comparison. The 0 Gy example is representative of comparison with a high proportion of identical values (e.g. 0 values), and the 0.7 Gy example present an inter-laboratory comparison where all participant laboratories overestimate the delivered dose. The statistic to compare the different algorithms was the z score statistic. The z score is a standardized measure of performance, calculated using the participant result x, the assigned value (xpt) and the standard deviation (σpt) (ISO 13528). The general expression to obtain a z score is: xpt and σpt are substituted for x* and s*. When prior fitness for purpose values is defined by the experts, the notations xffp and sffp are used (AMC 2015).