Objective: To present the development and validation processes of a decision aid for prostate cancer screening in Brazil.
Methods: Study with qualitative-participatory design for the elaboration of a decision aid for prostate cancer screening, with the participation of a group of men and physicians inserted in primary health care in 11 Brazilian states. Evidence synthesis, field testing, and use in clinical scenarios were performed to adapt the content, format, language, and applicability towards the needs of the target audience in the years 2018 and 2019. The versions were subsequently evaluated by the participants and modified based on the data obtained.
Results: We elaborated an unprecedented tool in Brazil, with information about the tests used in the screening, comparison of their possible benefits and harms and a numerical infographic with the consequences of this practice. We verified the decision aid usability to assist in the communication between the doctor and the man in the context of primary health care, besides identifying the need for greater discussion about sharing decisions in clinical scenarios.
Conclusion: The tool was easy to use, objective, and has little interference in consultation time. It is a technical-scientific material, produced by research, with the participation of its main target audience and which is available free of charge for use in Brazilian clinical scenarios.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8973024 | PMC |
| http://dx.doi.org/10.11606/s1518-8787.2022056003467 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Machine learning techniques for non-destructive estimation of plum fruit weight.
Sci Rep
January 2025
Crop and Horticultural Science Research Department, Mazandaran Agricultural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Tajrish, Iran.
Plum fruit fresh weight (FW) estimation is crucial for various agricultural practices, including yield prediction, quality control, and market pricing. Traditional methods for estimating fruit weight are often destructive, time-consuming, and labor-intensive. In this study, we addressed the problem of predicting plum FW using artificial intelligence (AI) methods based on fruit dimensions.
View Article and Find Full Text PDFMachine learning to predict the decision to perform surgery in hepatic echinococcosis.
HPB (Oxford)
December 2024
Fondazione IRCCS Policlinico San Matteo, SC Chirurgia Generale 1, Pavia, Italy. Electronic address:
Background: Cystic echinococcosis (CE) is a significant public health issue, primarily affecting the liver. While several management strategies exist, there is a lack of predictive tools to guide surgical decisions for hepatic CE. This study aimed to develop predictive models to support surgical decision-making in hepatic CE, enhancing the precision of patient allocation to surgical or non-surgical management pathways.
View Article and Find Full Text PDFReadability, quality and accuracy of generative artificial intelligence chatbots for commonly asked questions about labor epidurals: a comparison of ChatGPT and Bard.
Int J Obstet Anesth
December 2024
Department of Anesthesiology, 8700 Beverly Blvd #4209, Cedars-Sinai Medical Center, Los Angeles, CA 90064, United States. Electronic address:
Introduction: Over 90% of pregnant women and 76% expectant fathers search for pregnancy health information. We examined readability, accuracy and quality of answers to common obstetric anesthesia questions from the popular generative artificial intelligence (AI) chatbots ChatGPT and Bard.
Methods: Twenty questions for generative AI chatbots were derived from frequently asked questions based on professional society, hospital and consumer websites.
Visualizing hemodynamics: innovative graphical displays and imaging techniques in anesthesia and critical care.
Crit Care
January 2025
División de Terapia Intensiva, Hospital Juan A. Fernández, Buenos Aires, Argentina.
The advancements in cardiovascular imaging over the past two decades have been significant. The miniaturization of ultrasound devices has greatly contributed to their widespread adoption in operating rooms and intensive care units. The integration of AI-enabled tools has further transformed the field by simplifying echocardiographic evaluations and enhancing the reproducibility of hemodynamic measurements, even for less experienced operators.
View Article and Find Full Text PDFExplainable Machine Learning to Predict Treatment Response in Advanced Non-Small Cell Lung Cancer.
JCO Clin Cancer Inform
January 2025
Emory University School of Medicine, Atlanta, GA.
Purpose: Immune checkpoint inhibitors (ICIs) have demonstrated promise in the treatment of various cancers. Single-drug ICI therapy (immuno-oncology [IO] monotherapy) that targets PD-L1 is the standard of care in patients with advanced non-small cell lung cancer (NSCLC) with PD-L1 expression ≥50%. We sought to find out if a machine learning (ML) algorithm can perform better as a predictive biomarker than PD-L1 alone.
View Article and Find Full Text PDFWant 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!