Distributed Statistical Analyses: A Scoping Review and Examples of Operational Frameworks Adapted to Health Analytics.

Background: Data from multiple organizations are crucial for advancing learning health systems. However, ethical, legal, and social concerns may restrict the use of standard statistical methods that rely on pooling data. Although distributed algorithms offer alternatives, they may not always be suitable for health frameworks.

How to optimize and evaluate diversity in gynecologic cancer clinical trials: statements from the GCIG Barcelona Meeting.

Findings from clinical trials have led to advancement of care for patients with gynecologic malignancies. However, restrictive inclusion of patients into trials has been widely criticized for inadequate representation of the real-world population. Ideally, patients enrolled in clinical trials should represent a broader population to enhance external validity and facilitate translation of outcomes across all relevant groups.

From Treatment to Recovery: Gynecological Survivors' and Caregivers' Perspectives About the Usability of an Educational Resource.

The objective of this study was to understand gynecological cancer (GC) survivors' and their informal caregivers' perceptions about the usability of an educational resource to support their transition from primary cancer treatment into surveillance and/or recovery. After developing an empirical- and experiential-informed educational resource, we used a semi-structured questioning process to understand GC survivors and their caregivers' perceptions about its usability. Data were collected via online focus groups or 1:1 interviews that were audio recorded and transcribed.

Distributed Statistical Analyses: A Scoping Review and Examples of Operational Frameworks Adapted to Health Analytics.

Background: Data from multiple organizations are crucial for advancing learning health systems. However, ethical, legal, and social concerns may restrict the use of standard statistical methods that rely on pooling data. Although distributed algorithms offer alternatives, they may not always be suitable for health frameworks.

Predicting polymer solubility from phase diagrams to compatibility: a perspective on challenges and opportunities.

Polymer processing, purification, and self-assembly have significant roles in the design of polymeric materials. Understanding how polymers behave in solution (, their solubility, chemical properties, ) can improve our control over material properties their processing-structure-property relationships. For many decades the polymer science community has relied on thermodynamic and physics-based models to aid in this endeavor, but all rely on disparate data sets and use-case scenarios.