Publications by authors named "R M Campbell"

Introduction: In England, eligible adults aged 40-74 years are invited to attend a face-to-face (F2F) NHS Health Check appointment every 5 years. A digital version of the Health Check was introduced by a local authority as an alternative for those hesitant or less able to attend an F2F appointment.

Objectives: This qualitative study aimed to understand service users' (SUs) and healthcare professionals' (HCPs) experiences and opinions of F2F Health Checks and digital Health Checks (DHC), identify barriers and facilitators of the F2F Health Check and DHC pathways, and recommend potential improvements.

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Purpose: Nonselective fusion for adolescent idiopathic scoliosis results in greater correction of the Lumbosacral Takeoff Angle (LSTOA); however, there are patients selectively fused that still have considerable change in their LSTOA. We sought to identify the relationship between preoperative LSTOA flexibility and postoperative correction of the LSTOA.

Methods: This was a retrospective analysis of Lenke 1-6, lumbar B and C modifier patients in the Harms Study Group with 2-year follow-up.

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Chronic anxiety is commonly associated with poor sleep patterns, which may contribute to an increased risk of cardiovascular disease (CVD) through mechanisms like oxidative stress, vascular dysfunction, and poor blood pressure control. As sleep disturbances, particularly poor sleep quality and/or regularity, have been independently linked to CVD development, this study explored whether sleep quality/regularity in young adults with chronic anxiety are associated with early indicators of CVD risk, specifically oxidative stress, vascular function, and blood pressure control. Twenty-eight young (24±4 years) participants with a prior clinical diagnosis of generalized anxiety disorder (GAD) or elevated GAD symptoms (GAD7>10) had their sleep quality (total sleep time (TST) and sleep efficiency (SE)) and regularity (via TST/SE standard deviations (SD)) assessed for seven consecutive days.

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Background: Urinary tract infections (UTIs) are among the most common bacterial infections diagnosed in the emergency department. Treatment of UTIs is largely empiric because urine culture results are not rapidly available.

Objectives: We examined whether machine learning could predict antibiotic sensitivities of the urine cultures by using only data available during the clinical encounter.

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Purpose: To improve understanding of genitourinary symptoms (GUS) in women with breast cancer (BC).

Methods: Women with BC completed a survey assessing the type, severity, and impact of GUS experienced, and perceptions of treatment options.

Results: Surveys were completed by 506 women: median age 60 years (range 30 - 83).

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