Diagnostic uncertainty is common in clinical practice and affects both providers and patients on a daily basis. Yet, a unifying model describing uncertainty and identifying the best practices for how to teach about and discuss this issue with trainees and patients is lacking. In this paper, we explore the intersection of uncertainty and expertise. We propose a 2 × 2 model of diagnostic accuracy and certainty that can be used in discussions with trainees, outline an approach to communicating diagnostic uncertainty with patients, and advocate for teaching trainees how to hold such conversations with patients.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1515/dx-2018-0088 | DOI Listing |
Importance: Early detection of brain amyloidosis (Aβ+) is pivotal for diagnosing Alzheimer's disease (AD) and optimizing patient management, especially in light of emerging treatments. While plasma biomarkers are promising, their combined diagnostic value through specific ratios remains underexplored.
Objective: To evaluate the diagnostic accuracy of plasma pTau isoform (pTau181 and pTau217) to Aβ42 ratios in detecting Aβ+ status.
Brain Behav
January 2025
Department of Neurology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany.
Background: While automated methods for differential diagnosis of parkinsonian syndromes based on MRI imaging have been introduced, their implementation in clinical practice still underlies considerable challenges.
Objective: To assess whether the performance of classifiers based on imaging derived biomarkers is improved with the addition of basic clinical information and to provide a practical solution to address the insecurity of classification results due to the uncertain clinical diagnosis they are based on.
Methods: Retro- and prospectively collected data from multimodal MRI and standardized clinical datasets of 229 patients with PD (n = 167), PSP (n = 44), or MSA (n = 18) underwent multinomial classification in a benchmark study comparing the performance of nine machine learning methods.
Nord J Psychiatry
January 2025
Department of Child and Adolescent Psychiatry, Yildirim Beyazit University Yenimahalle Education and Research Hospital, Ankara, Turkey.
Background: Disruptive Mood Dysregulation Disorder (DMDD), characterized by severe irritability and temper outbursts, is a relatively new diagnosis included in the DSM-5. The study aimed to investigate the clinical characteristics, temperament, comorbidities, medication use, and sleep quality of children and adolescents diagnosed with DMDD and compare them with Major Depressive Disorder (MDD).
Methods: A total of 233 participants (DMDD: = 106; MDD: = 127) were assessed using the K-SADS-PL.
Jpn J Clin Oncol
January 2025
Department of Thoracic Oncology, Kansai Medical University, 2-3-1 Shinmachi, Hirakata city, Osaka 573-1191, Japan.
Background: Pre-cancer onset of cachexia raises uncertainties regarding the optimal timing for early intervention in lung cancer patients. We aimed to examine changes in physical function, nutritional status, and cachexia incidence in patients with lung cancer from the initial visit to treatment initiation and determine the effect of these changes on lung cancer treatment.
Methods: This single-center retrospective cohort study enrolled patients suspected of having advanced lung cancer who visited Kansai Medical University Hospital between January and February 2023 and were definitely diagnosed with the disease.
Bioelectromagnetics
January 2025
Seibersdorf Labor GmbH, Seibersdorf, Austria.
The electrical conductivity of human tissues is a major source of uncertainty when modelling the interactions between electromagnetic fields and the human body. The aim of this study is to estimate human tissue conductivities in vivo over the low-frequency range, from 30 Hz to 1 MHz. Noninvasive impedance measurements, medical imaging, and 3D surface scanning were performed on the forearms of ten volunteer test subjects.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!