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Researchers from the University of Toronto have developed a digital platform called Genetics Navigator to address the increasing demand for genetic services and the strain it puts on the standard model of genetic healthcare. The platform aims to support the collection of medical and family history, provide pregenetic and postgenetic counseling, and deliver genetic testing results. The effectiveness of Genetics Navigator will be evaluated through a randomized controlled trial involving 130 participants in Ontario genetics clinics. The primary outcome of the trial is participant distress two weeks after receiving test results, and secondary outcomes include knowledge, decisional conflict, anxiety, empowerment, quality of life, satisfaction, acceptability, digital health literacy, and health resource use. The results of the trial will be disseminated through various channels, including conferences and peer-reviewed journals. [Extracted from the article]

The article offers information on a recent report from the University of Novi Sad, Serbia, detailing the fundamentals of hypothesis testing and statistical test selection in clinical research. Topics discussed include the steps involved in hypothesis testing; the importance of choosing appropriate statistical tests; and the role of key statistical values like the p-value in interpreting study results.

A research study conducted by McMaster University in Canada explores the disparity between statistical and clinical significance in published clinical trials. The study highlights the ongoing criticism of the frequentist paradigm of null hypothesis statistics testing and its reliance on the p-value and statistical significance. The researchers aim to understand the extent of the problem and identify factors associated with discrepant results in these studies. The study will analyze a sample of 500 published randomized controlled trials between 2018 and 2022 to assess the clinical importance of trial results and determine the disparity between statistical and clinical significance. The research has received ethical approval and will be disseminated as a thesis, conference abstract, and peer-reviewed manuscript. [Extracted from the article]

DPC (Clustering by fast search and find of Density Peaks) algorithm and its variations typically employ Euclidean distance, overlooking the diverse contributions of individual feature to similarity and subsequent clustering. To address this limitation, the standard deviation weighted distance is proposed in this paper to enhance the Euclidean distance. This weighted distance takes into account the specific contribution of each feature to the distance (similarity) between data points. By utilizing this weighted distance, the local density ρ i and distance δ i of point i are defined, thereby capturing the local pattern of point i to the fullest extent possible. Outliers are defined using this innovative weighted distance. The divide and conquer assignment strategy is proposed based on this proposed weighted distance and the semi-supervised learning and the mutual K-nearest neighbor assumption. Consequently, the SFKNN-DPC (Standard deviation weighted distance and Fuzzy weighted K-Nearest Neighbors based Density Peak Clustering) algorithm is proposed, aiming to effectively uncover the hidden clusters within a dataset. Extensive experiments conducted on benchmark datasets demonstrate the superiority of SFKNN-DPC over DPC, its variations, and other benchmark clustering algorithms. Moreover, statistical significance tests indicate that SFKNN-DPC exhibits notable differences when compared to its counterparts. • Standard deviation weighted distance is proposed to enhance the Euclidean distance. • Local density ρ i and distance δ i of point i are defined utilizing the innovative distance, so do outliers and non-outliers. • Divide and conquer assignment strategy is proposed for assigning non-outliers and outliers in turn. • An innovative density peak clustering algorithm referred to SFKNN-DPC is proposed. • Extensive experiments demonstrate that SFKNN-DPC is superior to the peers in comparison. [ABSTRACT FROM AUTHOR]

The paper investigates a hypothesis testing problem concerning the potential additional contributions of other covariates to the structural function, given the known covariates. The structural function is the conditional expectation given covariates in which the response may depend on unknown nuisance functions. It includes classic regression functions and the conditional average treatment effects as illustrative instances. Based on Neyman's orthogonality condition, the proposed distance-based test exhibits the quasi-oracle property in the sense that the nuisance function asymptotically does not influence on the limiting distributions of the test statistic under both the null and alternatives. This novel test can effectively detect the local alternatives distinct from the null at the fastest possible rate in hypothesis testing. This is particularly noteworthy given the involvement of nonparametric estimation of the conditional expectation. Numerical studies are conducted to examine the performance of the test. In the real data analysis section, the proposed tests are applied to identify significantly explanatory covariates that are associated with AIDS treatment effects, yielding noteworthy insights. [ABSTRACT FROM AUTHOR]