Hemodynamic Failure Staging With Blood Oxygenation Level-Dependent Cerebrovascular Reactivity and Acetazolamide-Challenged (O-)HO-Positron Emission Tomography Across Individual Cerebrovascular Territories.

Background: Staging of hemodynamic failure (HF) in symptomatic patients with cerebrovascular steno-occlusive disease is required to assess the risk of ischemic stroke. Since the gold standard positron emission tomography-based perfusion reserve is unsuitable as a routine clinical imaging tool, blood oxygenation level-dependent cerebrovascular reactivity (BOLD-CVR) with CO is a promising surrogate imaging approach. We investigated the accuracy of standardized BOLD-CVR to classify the extent of HF.

Quantifying simultaneous innovations in evolutionary medicine.

To what extent do simultaneous innovations occur and are independently from each other? In this paper we use a novel persistent keyword framework to systematically identify innovations in a large corpus containing academic papers in evolutionary medicine between 2007 and 2011. We examine whether innovative papers occurring simultaneously are independent from each other by evaluating the citation and co-authorship information gathered from the corpus metadata. We find that 19 out of 22 simultaneous innovative papers do, in fact, occur independently from each other.

Machine Learning Algorithm Identifies Patients at High Risk for Early Complications After Intracranial Tumor Surgery: Registry-Based Cohort Study.

Introduction: Reliable preoperative identification of patients at high risk for early postoperative complications occurring within 24 h (EPC) of intracranial tumor surgery can improve patient safety and postoperative management. Statistical analysis using machine learning algorithms may generate models that predict EPC better than conventional statistical methods.

Objective: To train such a model and to assess its predictive ability.