Changes to ACA Individual Insurance Markets After States Leave Healthcare.gov 2016-2023.

To evaluate changes in enrollment, average risk scores, and premiums in the Affordable Care Act individual market after states transitioned from the federally facilitated marketplace (Healthcare.gov) to a state-based marketplace (SBM) between 2018 and 2023. This study employed a retrospective, quasi-experimental design of secondary data using a synthetic difference-in-differences analysis methodology.

Consensus on clinical outcome measures for lumbar spinal stenosis: recommendations from the ISSLS lumbar spinal stenosis taskforce.

Purpose: The purpose of this study was to determine through a Delphi process a list of outcomes measures for clinicians to use when assessing individuals with Lumbar Spinal Stenosis (LSS).

Methods: A three-phase Delphi process was conducted by the International Society for the Study of the Lumbar Spine (ISSLS) Lumbar Spinal Stenosis Taskforce, including two online surveys, two virtual meetings, and three in-person consensus meetings at the ISSLS annual conferences (2023-2025). Participants evaluated and ranked outcome measures for LSS, with final endorsement requiring > 66% agreement.

Simulation-Based Training of Surgical and Nonsurgical Skills in Orthopaedic Residency (Symposium Presented at the 2024 Summer CORD Conference).

Introduction: Modern orthopaedic residency training increasingly integrates knowledge, skills, and behavior (KSB), in line with updated American Board of Orthopaedic Surgery (ABOS) and Accreditation Council for Graduate Medical Education (ACGME) guidelines. Developments in simulation technology-including high-fidelity simulators, virtual reality, and data-driven assessment tools-enable programs to target both technical and non-technical competencies. This paper examines how innovations in simulation, curriculum design, and performance assessment are shaping the future of orthopaedic education.

A review of image processing and analysis of computed tomography images using deep learning methods.

The use of machine learning has seen extraordinary growth since the development of deep learning techniques, notably the deep artificial neural network. Deep learning methodology excels in addressing complicated problems such as image classification, object detection, and natural language processing. A key feature of these networks is the capability to extract useful patterns from vast quantities of complex data, including images.