Optimal allocation of antenatal and young child nutrition interventions: an individual-based global burden of disease calibrated microsimulation.

Background: Undernutrition remains a global crisis and is a focus of Sustainable Development Goals. While there are multiple known, effective interventions, complex interactions between prevention and treatment and resource constraints can lead to difficulties in allocating funding. Simulation studies that use in silico simulation can help illuminate the interactions between interventions and provide insight into the cost-effectiveness of alternative packages of options.

The Albright-Losee Low Profile Pivot Shift Test for ACL Deficiency.

Background: Recognizing ACL injuries on the field and in the office can be very challenging in awake and apprehensive patients. Despite high specificity, many published "pivot-shift" techniques have limited acceptance mainly because of unsatisfactory sensitivity. We describe in detail, four specific modifications and provide a critical review of our clinical experiences to empower the new user's readiness to master a novel screening procedure for ACL disruption.

Increased Risk of Patellofemoral Instability Events and Surgical Management in Patients With Joint Hypermobility Syndromes: A Matched Cohort Analysis.

Purpose: To compare the odds of patellofemoral instability events requiring subsequent surgery and revision surgical intervention in patients with joint hypermobility syndromes (JHS) to that of a matched cohort.

Methods: This is a retrospective cohort study using the PearlDiver Mariner Database. Records were queried between 2010 and 2021 with a diagnosis of JHS, including Ehlers-Danlos syndrome (EDS) and Marfan syndrome.

Simulated data for census-scale entity resolution research without privacy restrictions: a large-scale dataset generated by individual-based modeling.

Background: Entity resolution (ER) is the process of identifying and linking records that refer to the same real-world entity. ER is a fundamental challenge in data science, and a common barrier to ER research and development is that the data fields used for this fuzzy matching are personally identifiable information, such as name, address, and date of birth. The necessary restrictions on accessing and sharing these authentic data have slowed the work in developing, testing, and adopting new methods and software for ER.