This article examines an autonomous virtual patient (AVP) system for identifying differences between novices and experts in dentistry. The two groups in the study were ten boarded or board-eligible experts (seven males, three females; mean±sd age 40±11) and twenty-six fourth-year dental students (fifteen males, eleven females; mean±sd age 27±3), who were defined as novices. All participants interviewed and mock-examined four randomly selected AVPs who had either orofacial pain or an oral medicine problem; they then selected needed diagnostic tests, diagnoses, treatments, and medications. The mean misrecognition rate of the software was between 13 and 19 percent. Data collected were examined for a difference between the two groups (novices versus experts) on multiple variables. Significant group differences existed in the final total score, the number of diagnostic tests ordered, and the number of medications selected. Novices reported that they found virtual patients to be a valuable educational experience. These data demonstrated that experts and novices asked essentially the same questions and spent similar amounts of time with the patients, yet the experts consistently scored higher and ordered fewer diagnostic tests and medications than the novices.
Download full-text PDF |
Source |
---|
BMC Med Inform Decis Mak
January 2025
Great Ormond Street Institute of Child Health, University College London, London, UK.
Introduction: Unsupervised feature learning methods inspired by natural language processing (NLP) models are capable of constructing patient-specific features from longitudinal Electronic Health Records (EHR).
Design: We applied document embedding algorithms to real-world paediatric intensive care (PICU) EHR data to extract patient-specific features from 1853 patients' PICU journeys using 647 unique lab tests and medication events. We evaluated the clinical utility of the patient features via a K-means clustering analysis.
BMC Public Health
January 2025
Department of Disease Control, London School of Hygiene and Tropical Medicine, London, UK.
Background: Long-lasting insecticidal nets (LLINs) were once fully effective for the prevention of malaria; however, mosquitoes have developed resistance to pyrethroids, the main class of insecticides used on nets. Dual active ingredient LLINs (dual-AI LLINs) have been rolled out as an alternative to pyrethroid (PY)-only LLINs to counteract this. Understanding the minimum community usage at which these LLINs elicit an effect that also benefits non-users against malaria infection is important.
View Article and Find Full Text PDFBMC Gastroenterol
January 2025
Faculty of Medicine, University of Khartoum, Khartoum, 11111, Sudan.
Background & Objectives: Differentiation of histologic subtypes of appendiceal mucoceles may prove to be difficult on computed tomography (CT). The main objective of this study was to identify the CT features of mucocele of the appendix and correlate the imaging findings with histopathology in inflammatory, benign, and malignant neoplastic lesions, and whether these entities can be accurately differentiated on CT imaging.
Materials And Methods: CT scans of 31 patients with diagnosis of appendiceal mucocele were retrospectively reviewed and compared with histopathology.
BMC Cardiovasc Disord
January 2025
Graduate School of Public Health, St Luke's International University, Tokyo, Japan.
Background: Recent studies revealed an association between small kidney volume and progression of kidney dysfunction in particular settings such as kidney transplantation and transcatheter aortic valve implantation. We hypothesized that kidney volume was associated with the incidence of kidney-related adverse outcomes such as worsening renal function (WRF) in patients with acute heart failure (AHF).
Methods: This study was a single-center retrospective cohort study.
BMC Psychiatry
January 2025
West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.
The current DSM-oriented diagnostic paradigm has introduced the issue of heterogeneity, as it fails to account for the identification of the neurological processes underlying mental illnesses, which affects the precision of treatment. The Research Domain Criteria (RDoC) framework serves as a recognized approach to addressing this heterogeneity, and several assessment and translation techniques have been proposed. Among these methods, transforming RDoC scores from electronic medical records (EMR) using Natural Language Processing (NLP) has emerged as a suitable technique, demonstrating clinical effectiveness.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!