A patient first perspective of sleep disturbance across therapeutic areas: a systematic literature review of qualitative studies.

Background: Sleep, a vital pillar of health, impacts daily functioning and overall quality of life. Despite extensive research on sleep disturbances within specific therapeutic areas (TAs), there's limited understanding of how these disturbances affect patients across multiple TAs. This systematic literature review adopts a patient-centered approach to explore the meaningful aspects of health and concepts of interest relating to sleep and sleep disturbances across a variety of TAs, utilizing qualitative research to bridge the gap between patient experiences and clinical measures.

Corticosteroid injection up to 8 weeks prior to ACL reconstruction doubles the incidence of post-operative infection.

Purpose: This study was performed to determine if at least one corticosteroid injection (CSI) within 8-weeks prior to Anterior Cruciate Ligament Reconstruction (ACL-R) increases the incidence of postoperative infection.

Methods: A large globally federated research database (TriNetX) containing over 200 million patient records was retrospectively queried for patients undergoing ACL-R between October 1st, 2010, and January 1st, 2024 using diagnosis and procedure codes. A cohort of patients receiving CSI up to 8-weeks prior to ACL-R were compared to a cohort of patients who did not receive a CSI prior to ACL-R.

Engaging stakeholders to strengthen support for community-engaged research at Stanford School of Medicine: An institutional assessment and action planning approach.

Introduction: Despite the central role that patient and community engagement plays in translational science and health equity research, there remain significant institutional barriers for researchers and their community partners to engage in this work meaningfully and sustainably. The goal of this paper is to describe the process and outcomes of Engage for Equity PLUS at Stanford School of Medicine, which was aimed at understanding and addressing institutional barriers and facilitators for community-engaged research (CEnR).

Methods: A Stanford champion team of four faculty and two community partners worked with the University of New Mexico team to conduct two workshops ( = 26), focus groups ( = 2), interviews with leaders ( = 4), and an Institutional Multi-Stakeholder Survey ( = 35).

Dose prediction via deep learning to enhance treatment planning of lung radiotherapy including simultaneous integrated boost techniques.

Background: Recent studies have shown deep learning techniques are able to predict three-dimensional (3D) dose distributions of radiotherapy treatment plans. However, their use in dose prediction for treatments with varied prescription doses including simultaneous integrated boost (SIB), that is, using multiple prescription doses within the same plan, and benefit in improving plan quality should be validated.

Purpose: To investigate the feasibility and potential benefit of using deep learning to predict dose distribution of volumetric modulated arc therapy (VMAT) including SIB techniques and improve treatment planning for patients with lung cancer.