Differentiating Gliosarcoma from Glioblastoma: A Novel Approach Using PEACE and XGBoost to Deal with Datasets with Ultra-High Dimensional Confounders.

In this study, we used a recently developed causal methodology, called Probabilistic Easy Variational Causal Effect (PEACE), to distinguish gliosarcoma (GSM) from glioblastoma (GBM). Our approach uses a causal metric which combines Probabilistic Easy Variational Causal Effect (PEACE) with the XGBoost, or eXtreme Gradient Boosting, algorithm. Unlike prior research, which often relied on statistical models to reduce dataset dimensions before causal analysis, our approach uses the complete dataset with PEACE and the XGBoost algorithm.

Surgical training and capacity development in the South African internship programme.

Medical practitioners in South Africa manage a quadruple burden of disease. Junior doctors, who contribute significantly to the health workforce, must complete 2 years of internship training and 1 year of community service work in state health facilities after graduation to register as an independent medical practitioner. The aim of this article is to give a critical appraisal of the current national internship programme and why it was implemented, and outline suggestions for future changes.

Program evaluation of an adapted PEERS® social skills program in young adults with autism spectrum disorder and/or mild intellectual impairment and social skills difficulties.

Rationale, Aims And Objectives: Social challenges are common for young adults with autism spectrum disorder (ASD) and/or mild intellectual impairment, yet few evidence-based interventions exist to address these challenges. PEERS®, the Program for the Education and Enrichment of Relational Skills, has been shown to be effective in improving the social skills of young adults with ASD; however, it requires a significant time commitment for parents of young adults. As such, this mixed-methods study aimed to investigate the experiences of young adults, parents and PEERS® social coaches participating in an adapted PEERS® program, and to evaluate its acceptability and efficacy.

Reader reaction to "Outcome-adaptive lasso: Variable selection for causal inference" by Shortreed and Ertefaie (2017).

Shortreed and Ertefaie introduced a clever propensity score variable selection approach for estimating average causal effects, namely, the outcome adaptive lasso (OAL). OAL aims to select desirable covariates, confounders, and predictors of outcome, to build an unbiased and statistically efficient propensity score estimator. Due to its design, a potential limitation of OAL is how it handles the collinearity problem, which is often encountered in high-dimensional data.