Objective: The Association of American Medical Colleges defines recognition of the need for urgent or emergent escalation of care as a key Entrustable Professional Activity (EPA) for entering residency (EPA#10). This study pilots the use of an immersive virtual reality (VR) platform for defining objective observable behaviors as standards for evaluation of medical student recognition of impending respiratory failure.
Methods: A cross-sectional observational study was conducted from July 2018 to December 2019, evaluating student performance during a VR scenario of an infant in impending respiratory failure using the OculusRift VR platform. Video recordings were rated by 2 pair of physician reviewers blinded to student identity. One pair provided a consensus global assessment of performance (not competent, borderline, or competent) while the other used a checklist of observable behaviors to rate performance. Binary discriminant analysis was used to identify the observable behaviors that predicted the global assessment rating.
Results: Twenty-six fourth year medical students participated. Student performance of 8 observable behaviors was found to be most predictive of a rating of competent, with a 91% probability. Correctly stating that the patient required an escalation of care had the largest contribution toward predicting a rating of competent, followed by commenting on the patient's increased heart rate, low oxygen saturation, increased respiratory rate, and stating that the patient was in respiratory distress.
Conclusions: This study demonstrates that VR can be used to establish objective and observable performance standards for assessment of EPA attainment - a key step in moving towards competency based medical education.
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http://dx.doi.org/10.1016/j.acap.2020.10.010 | DOI Listing |
Viruses
December 2024
Department of Research, Altino Ventura Foundation (FAV), Recife 50070-040, Brazil.
Deformities, body asymmetries, and muscle contractures are common consequences of atypical postural patterns in children with c ongenital Zika syndrome (CZS). This study aimed to evaluate the posture of children with CZS, considering their neurological and visual impairments. Ophthalmological assessment included binocular best-corrected visual acuity (BCVA) using Teller Acuity Cards II (TAC II) and an ocular motility evaluation.
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December 2024
Friedrich-Loeffler-Institut Institute of Epidemiology, Südufer 10, 17493 Greifswald-Insel Riems, Germany.
African swine fever (ASF) emerged in Germany in 2020. A few weeks after the initial occurrence, infected wild boar were detected in Saxony. In this study, data from wild boar surveillance in Saxony were analyzed.
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December 2024
State Public Health Laboratory, Zapopan 45170, Jalisco, Mexico.
The coronavirus disease 2019 (COVID-19) pandemic profoundly disrupted the epidemiology of respiratory viruses, driven primarily by widespread non-pharmaceutical interventions (NPIs) such as social distancing and masking. This eight-year retrospective study examines the seasonal patterns and incidence of influenza virus, respiratory syncytial virus (RSV), and other respiratory viruses across pre-pandemic, pandemic, and post-pandemic phases in Jalisco, Mexico. Weekly case counts were analyzed using an interrupted time series (ITS) model, segmenting the timeline into these three distinct phases.
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December 2024
Centro de Investigaciones en Bioquímica Clínica e Inmunología (CIBICI)-Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Córdoba X5000HUA, Argentina.
Understanding the evolutionary patterns and geographic spread of SARS-CoV-2 variants, particularly Omicron, is essential for effective public health responses. This study focused on the genomic analysis of the Omicron variant in Cordoba, Argentina from 2021 to 2022. Phylogenetic analysis revealed the dominant presence of BA.
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November 2024
Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv 6997801, Israel.
In this study, we introduce a novel approach that integrates interpretability techniques from both traditional machine learning (ML) and deep neural networks (DNN) to quantify feature importance using global and local interpretation methods. Our method bridges the gap between interpretable ML models and powerful deep learning (DL) architectures, providing comprehensive insights into the key drivers behind model predictions, especially in detecting outliers within medical data. We applied this method to analyze COVID-19 pandemic data from 2020, yielding intriguing insights.
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