Relationship between age, conditioning intensity, and outcome after allografting in adults age ≥60 years with AML.

Methodological advancements now allow older adults with AML to receive allografts although conflicting data exist regarding relative outcomes across age groups and benefits of different conditioning intensities. We retrospectively analyzed 495 adults aged 60-64 (n = 184), 65-69 (n = 189), or ≥70 (n = 122) allografted for AML in remission at our institution from 2006 to 2023. There were no significant differences in relapse or relapse-free survival (RFS) among the 3 age cohorts after multivariable adjustment.

Community of Inquiry-Guided Design of Asynchronous Online Continuing Professional Development for Pediatric Nurses.

Background: Nurses play a pivotal role in the provision of health care for children across Africa. With limited pediatric nursing content in undergraduate nursing programs and few available pediatric postgraduate nursing programs, there is a need for additional continuing professional development opportunities to prepare nurses with the knowledge, skills, and confidence needed to care for children.

Method: To address this need, and mindful of the unique profile of potential participants, the Children's Nursing Development Unit at the University of Cape Town developed a suite of asynchronous short online courses.

REACH assessment of humans exposed to chemicals indirectly via the environment: screening modeling in EUSES versus state of the science.

The assessment of humans indirectly exposed to chemicals via the environment (HvE) is an assessment element of the Registration, Evaluation, Authorisation, and Restriction of Chemicals (REACH) regulation. The European Union System for the Evaluation of Substances (EUSES) is the default screening tool, aimed at prioritizing chemicals for further refinement/higher tier assessment. This review summarizes the approach used in EUSES, evaluates the state of the science in human exposure modeling via the environment, and identifies areas for further research to strengthen the confidence and applicability of EUSES for assessing HvE.

Large Language Models Outperform Traditional Natural Language Processing Methods in Extracting Patient-Reported Outcomes in Inflammatory Bowel Disease.

Background And Aims: Patient-reported outcomes (PROs) are vital in assessing disease activity and treatment outcomes in inflammatory bowel disease (IBD). However, manual extraction of these PROs from the free-text of clinical notes is burdensome. We aimed to improve data curation from free-text information in the electronic health record, making it more available for research and quality improvement.