A Prediction Model to Identify Clinically Relevant Medication Discrepancies at the Emergency Department (MED-REC Predictor): Development and Validation Study.

Background: Many patients do not receive a comprehensive medication reconciliation, mostly owing to limited resources. We hence need an approach to identify those patients at the emergency department (ED) who are at increased risk for clinically relevant discrepancies.

Objective: The aim of our study was to develop and externally validate a prediction model to identify patients at risk for at least 1 clinically relevant medication discrepancy upon ED presentation.

Assessment of anxiety in children with neurodevelopment disorders: Rasch analysis of the Spence Children's Anxiety Scale.

Anxiety is common in neurodevelopmental disorders (NDD). The parent version of the Spence Children's Anxiety Scale (SCAS-P) is a widely used measure to assess anxiety across a broad range of childhood populations. However, assessment of the measurement properties of the SCAS-P in NDDs have been limited.

The History of Diagnosing Venous Thromboembolism.

An accurate and prompt diagnosis of deep vein thrombosis and/or pulmonary embolism is important to prevent serious complications and mortality. Because the clinical presentation of venous thromboembolism (VTE) is often nonspecific, objective testing by means of radiological imaging is required to confirm the diagnosis. Historically, a diagnosis of VTE involved invasive imaging techniques like contrast venography or conventional pulmonary angiography.

Rasch analyses of the Quick Inventory of Depressive Symptomatology Self-Report in neurodegenerative and major depressive disorders.

Background: Symptoms of depression are present in neurodegenerative disorders (ND). It is important that depression-related symptoms be adequately screened and monitored in persons living with ND. The Quick Inventory of Depressive Symptomatology Self-Report (QIDS-SR) is a widely-used self-report measure to assess and monitor depressive severity across different patient populations.

FAIR in action: Brain-CODE - A neuroscience data sharing platform to accelerate brain research.

The effective sharing of health research data within the healthcare ecosystem can have tremendous impact on the advancement of disease understanding, prevention, treatment, and monitoring. By combining and reusing health research data, increasingly rich insights can be made about patients and populations that feed back into the health system resulting in more effective best practices and better patient outcomes. To achieve the promise of a learning health system, data needs to meet the FAIR principles of findability, accessibility, interoperability, and reusability.

Accuracy of physicians' intuitive risk estimation in the diagnostic management of pulmonary embolism: an individual patient data meta-analysis.

Background: In patients clinically suspected of having pulmonary embolism (PE), physicians often rely on intuitive estimation ("gestalt") of PE presence. Although shown to be predictive, gestalt is criticized for its assumed variation across physicians and lack of standardization.

Objectives: To assess the diagnostic accuracy of gestalt in the diagnosis of PE and gain insight into its possible variation.

Diagnostic Management of Acute Pulmonary Embolism in COVID-19 and Other Special Patient Populations.

Venous thromboembolism (VTE), in particular acute pulmonary embolism (PE), has been shown to be a frequent and potentially fatal complication of coronavirus disease 2019 (COVID-19). In response to the observed thrombotic complications, a large number of studies has been devoted to the understanding and management of COVID-19-associated coagulopathy. Notably, only a limited number of mostly retrospective studies has focused on the optimal diagnostic strategy for suspected PE in COVID-19 patients.