Choosing the appropriate measure of central tendency: mean, median, or mode?

Mean, median, and mode are among the most basic and consistently used measures of central tendency in statistical analysis and are crucial for simplifying data sets to a single value. However, there is a lack of understanding of when to use each metric and how various factors can impact these values. The aim of this article is to clarify some of the confusion related to each measure and explain how to select the appropriate metric for a given data set. The authors present this work as an educational resource, ensuring that these common statistical concepts are better understood throughout the Orthopedic research community.

Assessing the risk of thromboembolism in cancer patients receiving immunotherapy.

Malignancy has long been implicated with hypercoagulability, leading to an increased rate of both venous and arterial thromboembolic events (VTE and ATE). Immunotherapy has established itself as a cornerstone of modern cancer therapy by promoting antitumor immune responses, though there have been some suggestions that immune-related adverse events could include increased rates of VTE and ATE. In this review, we examine the available evidence regarding the use of immune checkpoint inhibitors (ICIs) and thrombosis. First, we describe the potential mechanisms by which ICIs might lead to thrombophilia given the overlap between the immune system, coagulation cascade, and platelet adhesion and activation. In addition, while there are some preclinical data evaluating immunotherapy-associated ATEs in animal models, there is a paucity of evidence exploring potential mechanism of VTEs in ICIs. Second, we review the incidence of ATE and VTE in patients receiving ICIs in the published literature. Finally, we discuss current limitations in understanding, areas of conflicting evidence, and approaches to further investigation.

Fragility Part I: a guide to understanding statistical power.

The aim of this paper is to close the knowledge-to-practice gap around statistical power. We demonstrate how four factors affect power: p value, effect size, sample size, and variance. This article further delves into the advantages and disadvantages of a priori versus post hoc power analyses, though we believe only understanding of the former is essential to addressing the present-day issue of reproducibility in research. Upon reading this paper, physician-scientists should have expanded their arsenal of statistical tools and have the necessary context to understand statistical fragility.