Purpose: Acute myeloid leukemia (AML) is a hematologic malignancy characterized by a poor prognosis but also a paradoxical possibility of cure. This renders decision-making complex and imminent. Unfortunately, many patients with AML misestimate their prognosis and treatment risk. While decision aids can improve illness understanding and reduce decisional conflict, there are no validated decision aids for AML. We developed and tested a novel AML decision aid (NCT03442452).

Methods: Patients (n = 20) were recruited at Duke University from May 2018 to February 2019. Participants completed assessments of AML knowledge and decisional conflict, before and after using the electronic decision aid. The primary endpoint was feasibility (endpoint met if > 80% of study participants completed all study components). Secondary analyses of efficacy were conducted using paired t tests for dependent pre-/post-samples.

Results: The primary endpoint of feasibility was met (100% of participants completed all study components). Secondary analyses showed improved knowledge and reduced decisional conflict after using the decision aid. Knowledge scores improved from a mean of 11.8 (out of 18) correct items at baseline to 15.1 correct items after using the decision aid (mean difference 3.35; p < 0.0001). Decisional conflict scores reduced significantly from baseline to post-test as well (mean difference - 6.5; p = 0.02).

Conclusion: These findings suggest that our AML decision aid is a useful tool to improve the patient experience and promote shared decision-making in AML. A randomized efficacy trial is planned.

Download full-text PDF

Source
http://dx.doi.org/10.1007/s00520-020-05864-5DOI Listing

Publication Analysis

Top Keywords

decision aid
24
decisional conflict
16
participants completed
12
acute myeloid
8
myeloid leukemia
8
efficacy trial
8
decision aids
8
aml decision
8
primary endpoint
8
endpoint feasibility
8

Similar Publications

Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data.

Sensors (Basel)

December 2024

School of Biological and Environmental Sciences, Liverpool John Moores University, James Parsons Building, Byrom Street, Liverpool L3 3AF, UK.

Camera traps offer enormous new opportunities in ecological studies, but current automated image analysis methods often lack the contextual richness needed to support impactful conservation outcomes. Integrating vision-language models into these workflows could address this gap by providing enhanced contextual understanding and enabling advanced queries across temporal and spatial dimensions. Here, we present an integrated approach that combines deep learning-based vision and language models to improve ecological reporting using data from camera traps.

View Article and Find Full Text PDF

Resistance spot-welded joints are crucial parts in contemporary manufacturing technology due to their ubiquitous use in the automobile industry. The necessity of improving manufacturing efficiency and quality at an affordable cost requires deep knowledge of the resistance spot welding (RSW) process and the development of artificial neural network (ANN)- and machine learning (ML)-based modelling techniques, apt for providing essential tools for design, planning, and incorporation in the welding process. Tensile shear force and nugget diameter are the most crucial outputs for evaluating the quality of a resistance spot-welded specimen.

View Article and Find Full Text PDF

Epidemiology of Rounding Error.

Medicina (Kaunas)

December 2024

Cooperative Studies Program Coordinating Center, VA Boston, Lafayette City Center, 2 Avenue de Lafayette, Boston, MA 02111, USA.

This work represents a significant contribution to understanding the importance of appropriately rounding numbers with minimal error. That is, to reduce inexact rounding and data truncation error and simultaneously eliminate unintentional misleading findings in epidemiological studies. The rounding of numbers represents a compromise solution that attempts to find a balance between the loss of information from reporting too few significant digits versus retaining more digits than necessary.

View Article and Find Full Text PDF

: Tobacco smoking is the most important cause of chronic diseases and premature death worldwide. Very brief advice (VBA) and brief advice (BA) represent evidence-based interventions designed to increase quit attempts. These are appropriate for all smokers, regardless of their motivation to quit, and involve several steps regarding the assessment, advice, and action.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!