Test-enhanced learning

BACKGROUND: The study aimed to assess the impact of different training modalities on otoscopy performance during a practical exam using a high-fidelity simulator and to determine if objective evaluation of otoscopy is feasible using a simulator that records insertion depth and tympanic membrane coverage. METHODS: Participants were assigned to one of four groups: control and three intervention groups with varying training approaches. Participants received otoscopy training and then were assessed through a practical exam on a high-fidelity simulator that uses virtual reality to visualize the ear canal and middle ear...

BACKGROUND: Although game-based applications have been used in disaster medicine education, no serious computer games have been designed specifically for training these nurses in an IEMT setting. To address this need, we developed a serious computer game called the IEMTtraining game. In this game, players assume the roles of IEMT nurses, assess patient injuries in a virtual environment, and provide suitable treatment options. METHODS: The design of this study is a retrospective comparative analysis...

BACKGROUND: Road traffic accidents (RTAs) are predicted to become the world's seventh leading cause of death by 2030. Given the significant impact of RTAs on public health, effective hospital preparedness plays a pivotal role in managing and mitigating associated health and life-threatening issues. This study aims to meticulously evaluate the preparedness of selected hospitals in western Iran to handle road traffic accidents with mass casualties (RTAs-MC). METHODS: The study employed a descriptive-analytical approach, utilizing a reliable and valid questionnaire to measure hospitals' preparedness levels...

The "schisandra-evodia" herb pair (S-E) is a herbal preparation to treat Alzheimer's disease (AD). This study aims to investigate the therapeutic efficacy and potential mechanism of S-E in AD rats, utilizing pharmacodynamic assessments and serum- and urine-based metabolomic analyses. Pharmacodynamic assessments included Morris water maze test, hematoxylin-eosin staining and immunohistochemistry experiments. The results of the study showed that the AD model was successful; the S-E significantly enhanced long-term memory and spatial learning in AD rats...

The role of self-assessment in workplace-based assessment remains contested. However, anaesthesia trainees need to learn to judge the quality of their own work. Entrustment scales have facilitated a shared understanding of performance standards among supervisors by aligning assessment ratings with everyday clinical supervisory decisions. We hypothesised that if the entrustment scale similarly helped trainees in their self-assessment, there would be substantial agreement between supervisor and trainee ratings...

It is now known that RNAs play more active roles in cellular pathways beyond simply serving as transcription templates. These biological mechanisms might be mediated by higher RNA stereo conformations, triggering the need to understand RNA secondary structures first. However, experimental protocols for solving RNA structures are unavailable for large-scale investigation due to their high costs and time-consuming nature. Various computational tools were thus developed to predict the RNA secondary structures from sequences...

Evaluation of human gait through smartphone-based pose estimation algorithms provides an attractive alternative to costly lab-bound instrumented assessment and offers a paradigm shift with real time gait capture for clinical assessment. Systems based on smart phones, such as OpenPose and BlazePose have demonstrated potential for virtual motion assessment but still lack the accuracy and repeatability standards required for clinical viability. Seq2seq architecture offers an alternative solution to conventional deep learning techniques for predicting joint kinematics during gait...

Due to its wide application, deep reinforcement learning (DRL) has been extensively studied in the motion planning community in recent years. However, in the current DRL research, regardless of task completion, the state information of the agent will be reset afterward. This leads to a low sample utilization rate and hinders further explorations of the environment. Moreover, in the initial training stage, the agent has a weak learning ability in general, which affects the training efficiency in complex tasks...

Federated recommender systems (FRSs), with their improved privacy-preserving advantages to jointly train recommendation models from numerous devices while keeping user data distributed, have been widely explored in modern recommender systems (RSs). However, conventional FRSs require transmitting the entire model between the server and clients, which brings a huge carbon footprint for cost-conscious cross-device learning tasks. While several efforts have been dedicated to improving the efficiency of FRSs, it's suboptimal to treat the whole model as the objective of compact design...

Wheelchair provision training is essential to overcome barriers related to the lack of knowledge of health professionals on this topic. Appropriate knowledge of the service provision process may lead to higher quality service and products, and thus be more likely to help people with mobility impairments achieve the fundamental human right of personal mobility. This study aimed to describe a training intervention for two groups of future physiotherapists in Colombia, assess cohort differences in performance on a knowledge test, and explore their post-training perceptions...

The initial enthusiasm about computational pathology (CP) and artificial intelligence (AI) was that they will replace pathologists entirely on the way to fully automated diagnostics. It is becoming clear that currently this is not the immediate model to pursue. On top of the legal and regulatory complexities surrounding its implementation, the majority of tested machine learning (ML)-based predictive algorithms do not display the exquisite performance needed to render them unequivocal, standalone decision makers for matters with direct implications to human health...

OBJECTIVES: The purpose of this study has been to evaluate the use of gamification in the classroom, in terms of its effects on attention, concentration, creativity, and generic capabilities, for university students enrolled in a Bachelor's degree program in Physiotherapy. METHODS: An experimental design was implemented, using three groups differentiated by their time of exposure to the game (0 min, 30 min, or 60 min per week). The sample consisted of 73 s-year students from a Bachelor's degree program in Physiotherapy...

The prediction of rock porosity and permeability is crucial for assessing reservoir productivity and economic feasibility. However, traditional methods for obtaining these properties are time-consuming and expensive, making them impractical for comprehensive reservoir evaluation. This study introduces a novel approach to efficiently predict rock porosity and permeability for reservoir assessment by leveraging real-time machine learning models. Utilizing readily available drilling parameters, this approach offers a cost-effective alternative to traditional time-consuming methods to predict formation petrophysical parameters in real-time...

Sexually transmitted infections (STIs) continue to pose a substantial public health challenge in the United States (US). Surveillance, a cornerstone of disease control and prevention, can be strengthened to promote more timely, efficient, and equitable practices by incorporating health information exchange (HIE) and other large-scale health data sources into reporting. New York City patient-level electronic health record data between January 1, 2018 and June 30, 2023 were obtained from Healthix, the largest US public HIE...

In the context of Alzheimer's disease (AD), timely identification is paramount for effective management, acknowledging its chronic and irreversible nature, where medications can only impede its progression. Our study introduces a holistic solution, leveraging the hippocampus and the VGG16 model with transfer learning for early AD detection. The hippocampus, a pivotal early affected region linked to memory, plays a central role in classifying patients into three categories: cognitively normal (CN), representing individuals without cognitive impairment; mild cognitive impairment (MCI), indicative of a subtle decline in cognitive abilities; and AD, denoting Alzheimer's disease...

OBJECTIVE: During the first year of the COVID-19 pandemic, most of the Centers for Disease Control and Prevention (CDC)'s National Breast and Cervical Cancer Early Detection Program (NBCCEDP) funded programs (recipients) experienced significant declines in breast and cervical cancer screening volume. However, 6 recipients maintained breast and/or cervical cancer screening volume during July-December 2020 despite their states' high COVID-19 test percent positivity. We led a qualitative multi-case study to explore these recipients' actions that may have contributed to screening volume maintenance...

The growing threat of antibiotic resistance necessitates accurate differentiation between bacterial and viral infections for proper antibiotic administration. In this study, a Virus vs. Bacteria machine learning model was developed to distinguish between these infection types using 16 routine blood test results, C-reactive protein concentration (CRP), biological sex, and age. With a dataset of 44,120 cases from a single medical center, the model achieved an accuracy of 82.2 %, a sensitivity of 79.7 %, a specificity of 84...

Conventional diagnostic methods for lung cancer, based on breath analysis using gas chromatography and mass spectrometry, have limitations for fast screening due to their limited availability, operational complexity, and high cost. As potential replacement, among several low-cost and portable methods, chemoresistive sensors for the detection of volatile organic compounds (VOCs) that represent biomarkers of lung cancer were explored as promising solutions, which unfortunately still face challenges. To address the key problems of these sensors, such as low sensitivity, high response time, and poor selectivity, this study presents the design of new chemoresistive sensors based on hybridised porous zeolitic imidazolate (ZIF-8) based metal-organic frameworks (MOFs) and laser-scribed graphene (LSG) structures, inspired by the architecture of the human lung...

Vitiligo is a hypopigmented skin disease characterized by the loss of melanin. The progressive nature and widespread incidence of vitiligo necessitate timely and accurate detection. Usually, a single diagnostic test often falls short of providing definitive confirmation of the condition, necessitating the assessment by dermatologists who specialize in vitiligo. However, the current scarcity of such specialized medical professionals presents a significant challenge. To mitigate this issue and enhance diagnostic accuracy, it is essential to build deep learning models that can support and expedite the detection process...

Seeing a face in motion can help subsequent face recognition. Several explanations have been proposed for this 'motion advantage', but other factors that might play a role have received less attention. For example, facial movement might enhance recognition by attracting attention to the internal facial features thereby facilitating identification. However, there is no direct evidence that motion increases attention to regions of the face that facilitate identification (i.e., internal features) compared to static faces...