Publications by authors named "E C Sayre"

Hattiesburg, Mississippi experienced two tornados within a four-year span (2013-2017). Community members who participated in response and recovery to both disasters were interviewed to understand how coordination changed between the two events. The purposive sample included representatives from a variety of organizational types, sizes, and missions.

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Objective: Osteoporosis, a known complication of rheumatoid arthritis (RA), increases the risk of hip fracture, which is associated with high morbidity and mortality. Fracture risk estimates in patients with RA treated with contemporary treatment strategies are lacking. The objectives were (1) estimate age-specific and sex-specific incidence rates and compare the risk of hip fractures in RA relative to age-matched and sex-matched general population controls, and (2) compare the risk of all-cause mortality in RA and general population controls after hip fracture.

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Article Synopsis
  • Spine surgery often leads to postoperative medical adverse events (AEs), primarily minor ones, which can be costly and impact patient outcomes; a study aimed to assess the effectiveness of a quality improvement (QI) care bundle in reducing these events.
  • The research spanned 14 years and compared outcomes before and after implementing the QI care bundle, analyzing nearly 13,500 patients to evaluate changes in AEs and associated costs.
  • Results indicated a significant reduction in several types of AEs, such as cardiac and pulmonary issues, following QI implementation; however, some AEs, like delirium, did not show improvement.
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Background: Colorectal cancer (CRC) is estimated to be the fourth most common cancer diagnosis in Canada (except for nonmelanoma skin cancers) and the second and third leading cause of cancer-related death in male and female individuals, respectively.

Objective: The rising incidence of early age-onset colorectal cancer (EAO-CRC; diagnosis at less than 50 years) calls for a better understanding of patients' pathway to diagnosis. Therefore, we evaluated patterns of prescription medication use before EAO-CRC diagnosis.

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Background: Machine learning (ML) algorithms can accurately estimate left ventricular ejection fraction (LVEF) from echocardiography, but their performance on cardiac point-of-care ultrasound (POCUS) is not well understood.

Objectives: We evaluate the performance of an ML model for estimation of LVEF on cardiac POCUS compared with Level III echocardiographers' interpretation and formal echo reported LVEF.

Methods: Clinicians at a tertiary care heart failure clinic prospectively scanned 138 participants using hand-carried devices.

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