No abstract available.
Thermography

In most medical research the P value is commonly used to describe test results. Because the power of statistical test is influenced by sample size, the null hypothesis can be rejected (P<0.05) in most cases if the sample size is tremendously big even if the real difference (or relationship) is extremly small. To overcome the weakness of using the P value, effect size can be used in the statistical analysis. Effect size can be defined as the "degree to which the phenomenon (difference or relationship) is present in the population". The effect size is used in sample size calculation, data interpretation and conducting meta-analysis. This manuscript describes limitations in using the P value and further introduces the concept of effect size.
Data Interpretation, Statistical ; Research Design ; Sample Size

Obturator nerve block has been commonly used for pain management to prevent involuntary reflex of the adductor thigh muscles. One of several options for this block is chemical neurolysis. Neurolysis is done with chemical agents. Chemical agents used in the neurolysis of the obturator nerve have been alcohol, phenol, and botulinum toxin. In the current case, a patient with spasticity of the adductor thigh muscle due to cervical cord injury had obturator nerve neurolysis done with botulinum toxin type B (BoNT-B). Most of the previous studies have used BoNT-A with only a few reports that have used BoNT-B. BoNT-B has several advantages and disadvantages over BoNT-A. Thus, we report herein a patient who successfully received obturator nerve neurolysis using BoNT-B to treat adductor thigh muscle spasm.
Botulinum Toxins ; Botulinum Toxins, Type A ; Humans ; Muscle Spasticity ; Muscles ; Nerve Block ; Obturator Nerve ; Pain Management ; Phenol ; Reflex ; Spasm ; Thigh

Conventional statistical tests are usually called parametric tests. Parametric tests are used more frequently than nonparametric tests in many medical articles, because most of the medical researchers are familiar with and the statistical software packages strongly support parametric tests. Parametric tests require important assumption; assumption of normality which means that distribution of sample means is normally distributed. However, parametric test can be misleading when this assumption is not satisfied. In this circumstance, nonparametric tests are the alternative methods available, because they do not required the normality assumption. Nonparametric tests are the statistical methods based on signs and ranks. In this article, we will discuss about the basic concepts and practical use of nonparametric tests for the guide to the proper use.
Data Interpretation, Statistical ; Investigative Techniques ; Statistics, Nonparametric

No abstract available.

Using diagnostic testing to determine the presence or absence of a disease is essential in clinical practice. In many cases, test results are obtained as continuous values and require a process of conversion and interpretation and into a dichotomous form to determine the presence of a disease. The primary method used for this process is the receiver operating characteristic (ROC) curve. The ROC curve is used to assess the overall diagnostic performance of a test and to compare the performance of two or more diagnostic tests. It is also used to select an optimal cut-off value for determining the presence or absence of a disease. Although clinicians who do not have expertise in statistics do not need to understand both the complex mathematical equation and the analytic process of ROC curves, understanding the core concepts of the ROC curve analysis is a prerequisite for the proper use and interpretation of the ROC curve. This review describes the basic concepts for the correct use and interpretation of the ROC curve, including parametriconparametric ROC curves, the meaning of the area under the ROC curve (AUC), the partial AUC, methods for selecting the best cut-off value, and the statistical software to use for ROC curve analyses.