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The results of the questionnaires are converted into frequencies and percentages for binary and polytomous variables and averages for continuous variables.
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Meta-analyses were conducted for eligible studies by combining study specific estimates using the inverse variance method with weighted average for continuous outcomes in a random effects model.
Study specific estimates were combined using the inverse variance method with weighted average for continuous outcomes in a random effects model.
We explored the heat-wave effects with adjustment for two-day moving averages of continuous apparent temperature on the current day and the previous day, which was modeled as a natural spline with 6 degrees of freedom.
We use the average relative error (for continuous variables) and misclassification error (for categorical variables) to assess the imputation performance for both the logistic and random forest models.
Data were presented in tables of frequency distribution for discrete variables, and using the average and standard deviation for continuous variables.
The results were expressed in absolute numbers and percentages for discrete variables and in average ± standard deviation for continuous variables.
Data were given as average values and SDs for continuous variables or as percentages for discrete variables.
Scan start points were detected and marked (using average gradient and criterion for continuous scans) and corrected (using template drift correction).
For continuous variables, averages, frequencies and percentages were calculated.
For continuous variables average values and standard deviation (SD) were calculated.
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