PURPOSE: Postmenopausal women are a unique population with high risk for the degeneration of muscle. The aim of the present meta-analysis was to accurately evaluate the effects of vitamin D on muscular strength in postmenopausal women.METHODS: A review was conducted using electronic databases, including PubMed, EMBASE, Ovid Medline, CINAHL complete, and the Cochrane Library from inception through 19 March 2019. Included studies were selected by two independent reviewers. The meta-analysis were performed using Review Manager 5.3 software.RESULTS: A total of nine randomized controlled clinical trials were included in this review. Vitamin D interventions led to no changes in the upper limb muscle strength (mean difference –0.16, 95% CI: –1.09 to 0.77), lower body muscle strength (standard mean difference 0.08, 95% CI: –0.11 to 0.26), and back/hip muscle strength (standard mean difference 0.06, 95% CI: -0.05 to 0.17).CONCLUSION: Pooled results from eight studies indicated that supplementation of vitamin D did not increase muscle strength in postmenopausal women. Apparently, the present review suggests that supplementation of vitamin D alone had no didn't show any beneficial effects on muscle strength in postmenopausal women.
Female ; Humans ; Meta-Analysis as Topic ; Muscle Strength ; Postmenopause ; Upper Extremity ; Vitamin D ; Vitamins

PURPOSE: To explain the concept and procedure of random allocation as used in a randomized controlled study. METHODS: We explain the general concept of random allocation and demonstrate how to perform the procedure easily and how to report it in a paper.
Humans ; Random Allocation ; Randomized Controlled Trials as Topic/*methods

No abstract available.
Data Accuracy ; Data Interpretation, Statistical ; Statistics as Topic ; Biomedical Research ; Analysis of Variance

The appropriate plot effectively conveys the author's conclusions to readers. Journal of Korean Medical Science will provide a series of special articles to show you how to make consistent and excellent plots easier. In the first of this series of special articles, I will cover Kaplan-Meier curve (or Kaplan-Meier plot) and the ease tools. This plot, generated as a result of the Survival Analysis, provides a visualization of the ‘Kaplan-Meier Survival Probability Estimate’ for each group.
Survival Analysis

Purpose: This study examined the subjective frame of patient satisfaction in the comprehensive nursing care services and explored future directions by investigating subjective frame types. Methods: P-Samples were collected from 30 patients who underwent the comprehensive nursing care service. To enable a forced normal distribution, 31 Q-samples were classified on a 9-point scale considering the degree of agreement. The collected data were analyzed by principal component factor analysis and varimax rotation using the PQ method program. Results: The subjective frame of patient satisfaction with the comprehensive nursing care service was divided into four types. Considering the characteristics of each, the types were labeled as “the formation of relationships with the surroundings”, “autonomous decision-making”, “non-verbal empathy”, and “therapeutic support resources”. Each type can be classified into therapeutic relationship and therapeutic process categories. Conclusion This study revealed changes in expectations regarding new medical services and that the subjective frames that affect the formation of satisfaction are diverse. The study results are expected to be used as basic data to develop strategies for creating a therapeutic environment that improves patient satisfaction in the comprehensive nursing care service.

The appropriate plot effectively conveys the author's conclusions to the readers. The Journal of Korean Medical Science provides a series of special articles to show you how to make consistent and excellent plots easier. In the second article, drawing receiver operating characteristic (ROC) curve is introduced. A ROC curve is a graphic plot that illustrates the diagnostic ability as its discrimination threshold is varied. It is widely used as logistic regression analysis as machine learning becomes widespread. It has great visual effect in comparing various diagnostic tools.

Boxer's knuckle has become known as rare diseases caused by trauma, congenital, idiopathic, degenerative and more. Because there is no research for specific prevalence rate, the purpose of this study is to figure out the possibility of the prevalence rate of Korean early 20's who relied on data from the Internet questionnaire about the group suspected of having Boxer's knuckle by self-diagnosis after an indirect explanation for Boxer's knuckle. The positive response rate of Boxer's knuckle analyzed by a public Internet survey questionnaire contains personal information, Boxer's knuckle symptoms, location, risk factors, family history, and so on. Total of 304 respondents include 106 males (median age, 23 years; Q1, 21–Q3, 23), 198 females (median age, 21 years; Q1, 21–Q3, 23). Thirty-three patients had severe symptoms of Boxer's knuckle (dislocation) and 42 patients had mild symptoms of Boxer's knuckle (subluxation). Sex, age, type of exercise and duration of exercise, family history, etc were analyzed. It is a result that whether or not history of severe exercise and family history are statistical significances as risk factors. Fifteen (7.7%) of 196 people who do not exercise, seven (14.0%) of 50 people with light exercise, 11 (19.0%) of 58 people with severe exercise, 28 (9.5%) of 294 people without family history and five (50.0%) of 10 people with family history showed symptoms of the Boxer's knuckle. The positive response rate of the Boxer's knuckle is 10.9%. The exercise in the possibility of injury and family history are significant predictors.
Female ; Humans ; Internet* ; Male ; Metacarpophalangeal Joint ; Prevalence* ; Rare Diseases ; Risk Factors ; Surveys and Questionnaires

The appropriate plot effectively conveys the author's conclusions to the readers. JKMS will provide a series of special articles to show you how to make consistent and excellent plots easier. In this article, we will cover plots with categorical data. We will show what ‘Bubble Plot,’ ‘Matrix Bubble Plot,’ and ‘Matrix Bar Plot’ are and how to make them.

WHY: Calculating the sample size is essential to reduce the cost of a study and to prove the hypothesis effectively. HOW: Referring to pilot studies and previous research studies, we can choose a proper hypothesis and simplify the studies by using a website or Microsoft Excel sheet that contains formulas for calculating sample size in the beginning stage of the study. MORE: There are numerous formulas for calculating the sample size for complicated statistics and studies, but most studies can use basic calculating methods for sample size calculation.
Chi-Square Distribution ; *Research Design ; *Sample Size ; Statistics as Topic/*methods

The appropriate plot effectively conveys the author’s conclusions to the readers. Journal of Korean Medical Science will provide a series of special articles to show you how to more easily make consistent and excellent plots. In this article, we will cover plots with numeric data.