Unsupervised machine learning methods are important analytical tools that can facilitate the analysis and interpretation of high-dimensional data. Unsupervised machine learning methods identify latent patterns and hidden structures in highdimensional data and can help simplify complex datasets. This article provides an overview of key unsupervised machine learning techniques including K-means clustering, hierarchical clustering, principal component analysis, and factor analysis. With a deeper understanding of these analytical tools, unsupervised machine learning methods can be incorporated into health sciences research to identify novel risk factors, improve prevention strategies, and facilitate delivery of personalized therapies and targeted patient care.
Due to its frequent misuse, the p value has become a point of contention in the research community. In this editorial, we seek to clarify some of the common misconceptions about p values and the hazardous implications associated with misunderstanding this commonly used statistical concept. This article will discuss issues related to p value interpretation in addition to problems such as p-hacking and statistical fragility; we will also offer some thoughts on addressing these issues. The aim of this editorial is to provide clarity around the concept of statistical significance for those attempting to increase their statistical literacy in Orthopedic research.
Background
Traditionally, wire cerclage closure has been used to reapproximate the sternum after cardiac surgery. Recent evidence suggests that rigid sternal fixation may reduce the risk of wound complications. The aim of this study was to analyze our 10‐year experience with longitudinal rigid sternal fixation (LRSF) for prevention and treatment of wound complications in high‐risk patients.
Methods
We reviewed data from cardiac surgical database of patients who underwent LRSF, and compared their outcomes with conventional wire cerclage closure (CWS). Among these 319 patients were designated as having high‐risk for the development of deep wound complications and received primary LRSF (treatment group). We matched their outcomes with 319 patients who met indications for LRSF however, underwent standard closure with CWC (control group).
Results
Both groups were comparable regarding preoperative and intraoperative variables. The benefit observed among matched patients who had undergone LRSF was largely driven by a decreased rate of deep wound infections (0.63% vs. 3.45% vs., p < .01), 30‐day mortality (1.57% vs. 5.96%) and hospital length (8.2 vs. 11.7 days) p < .05, respectively. A multivariate logistic regression analysis found four independent risk factors for the development of sternal dehiscence. Sternal healing evaluated by computerized tomography scan using 6‐point scale at 3 months after surgery was superior in LRSF patients. Pain scores were significantly lower in LRSF patients as well.
Conclusions
In patients with an increased risk for sternal instability and wound infections after cardiac surgery, sternal reconstruction using LRSF is an effective technique to stabilize sternum for preventive and treatment purposes.
In recent decades, new information has arisen regarding sternal healing and extended indications for using rigid plate fixation in patients during cardio-thoracic procedures. Three randomized controlled multicenter clinical trials recently demonstrated positive results after rigid plate fixation, including reduced sternal complications and decreased length of hospital stay. However, redo-sternotomy after sternal reconstruction utilizing rigid fixation has not been previously delineated in surgical literature. This case highlights the technical challenges of performing a median sternotomy for cardiac surgery after sternal reconstruction with bilateral longitudinal plating.
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.
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