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It is complex and tedious to calculate, although web-based calculators are available to do this fairly easily. Many nephrologists have difficulty understanding it. Urea is not associated with toxicity. [4] Standardized Kt/V only models the clearance of urea and thus implicitly assumes the clearance of urea is comparable to other toxins.
where C is the concentration [mol/m 3]; t is the time [s]; K is the clearance [m 3 /s]; V is the volume of distribution [m 3]; From the above definitions it follows that is the first derivative of concentration with respect to time, i.e. the change in concentration with time.
Most two-sample t-tests are robust to all but large deviations from the assumptions. [22] For exactness, the t-test and Z-test require normality of the sample means, and the t-test additionally requires that the sample variance follows a scaled χ 2 distribution, and that the sample mean and sample variance be statistically independent ...
The normal range of GFR, adjusted for body surface area, is 100–130 average 125 (mL/min)/(1.73 m 2) in men and 90–120 (mL/min)/(1.73 m 2) in women younger than the age of 40. In children, GFR measured by inulin clearance is 110 (mL/min)/(1.73 m 2) until 2 years of age in both sexes, and then it progressively decreases. After age 40, GFR ...
Usually, clearance is measured in L/h or mL/min. [2] Excretion, on the other hand, is a measurement of the amount of a substance removed from the body per unit time (e.g., mg/min, μg/min, etc.). While clearance and excretion of a substance are related, they are not the same thing.
In statistics, Welch's t-test, or unequal variances t-test, is a two-sample location test which is used to test the (null) hypothesis that two populations have equal means. It is named for its creator, Bernard Lewis Welch , and is an adaptation of Student's t -test , [ 1 ] and is more reliable when the two samples have unequal variances and ...
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The Student's t distribution plays a role in a number of widely used statistical analyses, including Student's t test for assessing the statistical significance of the difference between two sample means, the construction of confidence intervals for the difference between two population means, and in linear regression analysis.