Not All Frailty Assessments Are Created Equal: Comparability of Electronic Health Data-Based Frailty Assessments in Assessing Older People in Residential Care.

Objectives: To evaluate the comparability of frailty assessment tools - the electronic frailty index (eFI), retrospective electronic frailty index (reFI), and clinical frailty scale (CFS) - in older residents of care facilities. Methods: Data from 813 individuals aged 65 or older, with frailty and co-morbidities, collected between 2022 and 2023, were analysed using various statistical methods. Results: The results showed significant differences in frailty classification among the tools: 78.3% were identified as moderately to severely frail by eFI, 59.6% by reFI, and 92.1% by CFS. Statistical tests confirmed significant differences (p < .05) in their assessments, indicating variability in measurement methods. Discussion: This study advances the understanding of frailty assessment within aged-care settings, highlighting the differences in the efficacy of these assessment tools. It underscores the challenges in frailty assessments and emphasizes the need for continuous refinement of assessment methods to address the diverse facets of frailty in aged care.

Less contrast, more clarity, innovative visualization technique for management of multiple colorectal liver metastases using microwave ablation through a portal venous access.

Colorectal cancer, a leading cause of cancer-related deaths, often results in liver metastases, with about half of patients affected. For those ineligibles for surgery, percutaneous microwave ablation (MWA) offers a viable alternative. Conventionally, visualizing liver lesions prior to MWA demands significant IV contrast, often needing repeated sessions. We introduce a technique using minimal IV contrast to treat multiple metastatic lesions simultaneously. A case of a 47-year-old male with stage 4 colorectal adenocarcinoma and multiple liver metastases is presented. Instead of the typical 100-150 cc of IV contrast, our method used just 25 cc, successfully ablating 6 hepatic metastases in 1 session. This approach not only reduces contrast volume but also optimizes treatment efficiency.

Traumatic atlantoaxial rotatory fixation in adults: a systematic review of published cases.

Atlantoaxial rotatory fixation (AARF) in adults is a rare and clinically challenging condition characterized by a spectrum of etiological factors, predominantly attributed to traumatic and inflammatory pathologies within the craniovertebral region. Trauma is the most frequently identified cause within the adult population, with the first case report published in 1907. This study aims to conduct a systematic review that addresses the clinical presentations and management strategies relating to traumatic atlantoaxial rotatory fixation in adults. A comprehensive search of the PubMed database was executed, adhering to the PRISMA guidelines. The inclusion criteria encompassed case reports and series documenting AARF cases in individuals aged 18 and above, spanning database inception to July 2022. Studies not published in the English language were excluded. A total of 61 articles reporting cases of AARF in the adult population were included in the study. The mean age of affected individuals was 36.1 years (± 15.6), with a distribution of 46% females and 54% males. Predominant mechanisms of injury included motor vehicle accidents and falls, constituting 38% and 22% of cases, respectively. Among the classification systems employed, Fielding and Hawkins type I accounted for the majority at 63%, followed by type II at 10%, and type III at 4%. Conservative management was used for treatment in 65% of acute (65%) cases and 29% of chronic cases. Traumatic AARF is a rare phenomenon in the adult population, is more common in younger adults, and does not often present with neurologic deficits. Patients diagnosed acutely are more likely to be successfully treated with conservative management, while patients diagnosed chronically are less likely to be reduced with conservatively and often require surgical treatment. Surgery should be considered for patients with irreducible dislocations, ligamentous injuries, unstable associated fractures, and persistent pain resistant to conservative management.

Ensemble machine learning model trained on a new synthesized dataset generalizes well for stress prediction using wearable devices.

INTRODUCTION: Advances in wearable sensor technology have enabled the collection of biomarkers that may correlate with levels of elevated stress. While significant research has been done in this domain, specifically in using machine learning to detect elevated levels of stress, the challenge of producing a machine learning model capable of generalizing well for use on new, unseen data remain. Acute stress response has both subjective, psychological and objectively measurable, biological components that can be expressed differently from person to person, further complicating the development of a generic stress measurement model. Another challenge is the lack of large, publicly available datasets labeled for stress response that can be used to develop robust machine learning models. In this paper, we first investigate the generalization ability of models built on datasets containing a small number of subjects, recorded in single study protocols. Next, we propose and evaluate methods combining these datasets into a single, large dataset to study the generalization capability of machine learning models built on larger datasets. Finally, we propose and evaluate the use of ensemble techniques by combining gradient boosting with an artificial neural network to measure predictive power on new, unseen data. In favor of reproducible research and to assist the community advance the field, we make all our experimental data and code publicly available through Github at https://github.com/xalentis/Stress. This paper's in-depth study of machine learning model generalization for stress detection provides an important foundation for the further study of stress response measurement using sensor biomarkers, recorded with wearable technologies. METHODS: Sensor biomarker data from six public datasets were utilized in this study. Exploratory data analysis was performed to understand the physiological variance between study subjects, and the complexity it introduces in building machine learning models capable of detecting elevated levels of stress on new, unseen data. To test model generalization, we developed a gradient boosting model trained on one dataset (SWELL), and tested its predictive power on two datasets previously used in other studies (WESAD, NEURO). Next, we merged four small datasets, i.e. (SWELL, NEURO, WESAD, UBFC-Phys), to provide a combined total of 99 subjects, and applied feature engineering to generate additional features utilizing statistical summaries, with sliding windows of 25 s. We name this large dataset, StressData. In addition, we utilized random sampling on StressData combined with another dataset (EXAM) to build a larger training dataset consisting of 200 synthesized subjects, which we name SynthesizedStressData. Finally, we developed an ensemble model that combines our gradient boosting model with an artificial neural network, and tested it using Leave-One-Subject-Out (LOSO) validation, and on two additional, unseen publicly available stress biomarker datasets (WESAD and Toadstool). RESULTS: Our results show that previous models built on datasets containing a small number (<50) of subjects, recorded in single study protocols, cannot generalize well to new, unseen datasets. Our presented methodology for generating a large, synthesized training dataset by utilizing random sampling to construct scenarios closely aligned with experimental conditions demonstrate significant benefits. When combined with feature-engineering and ensemble learning, our method delivers a robust stress measurement system capable of achieving 85% predictive accuracy on new, unseen validation data, achieving a 25% performance improvement over single models trained on small datasets. The resulting model can be used as both a classification or regression predictor for estimating the level of perceived stress, when applied on specific sensor biomarkers recorded using a wearable device, while further allowing researchers to construct large, varied datasets for training machine learning models that closely emulate their exact experimental conditions. CONCLUSION: Models trained on small, single study protocol datasets do not generalize well for use on new, unseen data and lack statistical power. Machine learning models trained on a dataset containing a larger number of varied study subjects capture physiological variance better, resulting in more robust stress detection. Feature-engineering assists in capturing these physiological variance, and this is further improved by utilizing ensemble techniques by combining the predictive power of different machine learning models, each capable of learning unique signals contained within the data. While there is a general lack of large, labeled public datasets that can be utilized for training machine learning models capable of accurately measuring levels of acute stress, random sampling techniques can successfully be applied to construct larger, varied datasets from these smaller sample datasets, for building robust machine learning models.

Endovascular management of a pulmonary artery pseudoaneurysm secondary to mucormycosis: A case report.

The development of pulmonary artery pseudoaneurysm (PAP) secondary to pulmonary mucormycosis (PM) is exceedingly rare. Without immediate intervention, PAPs can result in life-threatening hemorrhage as these weakening vessels are prone to rupture. To avoid such an occurrence, procedures that restrict blood flow to the vulnerable region are typically performed. The present case study details the effective employment of endovascular coil embolization in treating a patient with PAP due to pulmonary mucormycosis.

Forecasting emergency department waiting time using a state space representation.

The provision of waiting time information in emergency departments (ED) has become an increasingly popular practice due to its positive impact on patient experience and ED demand management. However, little scientific attention has been given to the quality and quantity of waiting time information presented to patients. To improve both aspects, we propose a set of state space models with flexible error structures to forecast ED waiting time for low acuity patients. Our approach utilizes a Bayesian framework to generate uncertainties associated with the forecasts. We find that the state-space models with flexible error structures significantly improve forecast accuracy of ED waiting time compared to the benchmark, which is the rolling average model. Specifically, incorporating time-varying and correlated error terms reduces the root mean squared errors of the benchmark by 10%. Furthermore, treating zero-recorded waiting times as unobserved values improves forecast performance. Our proposed model has the ability to provide patient-centric waiting time information. By offering more accurate and informative waiting time information, our model can help patients make better informed decisions and ultimately enhance their ED experience.

Simulation Training in the Management of Adverse Contrast Reactions: A Systematic Review.

BACKGROUND: Simulation-based training has become increasingly prominent within medical education, but its utility within radiology remains underexplored. OBJECTIVE: To appraise the evidence for the effectiveness of simulation on the management of adverse reactions to contrast media. METHODS: A systematic search of the literature was conducted. Eligible studies recruited radiology residents, provided simulation-based training focused on contrast reaction management, and measured any effectiveness outcome compared with any nonsimulation training or no training. The quality of studies was appraised and outcomes were classified according to Kirkpatrick's hierarchy and the strength of evidence. RESULTS: Out of 146 screened results, 15 articles were included that described 17 studies-3 randomized trials and 14 pretest-posttest studies of hands-on or, less commonly, computer-based simulation. In all 16 studies that assessed knowledge before and after intervention, written test scores improved after simulation. Most studies noted improvements in comfort or confidence managing contrast reactions as well. In all three studies that assessed knowledge after simulation and after didactic lecture as a control, posttest scores were not statistically significantly better in the simulation groups than the lecture groups. Common study limitations included single-group designs, measuring only learning outcomes using unvalidated instruments, modest sample sizes, and limited assessment of long-term retention. CONCLUSION: Simulation produces subjective improvements and knowledge gain relevant to contrast reaction management. Further research is required to demonstrate superiority of simulation-based contrast reaction management training over traditional didactic lecture-based instruction.

High risk lung nodule: A multidisciplinary approach to diagnosis and management.

Pulmonary nodules are often discovered incidentally during CT scans performed for other reasons. While the vast majority of nodules are benign, a small percentage may represent early-stage lung cancer with the potential for curative treatments. With the growing use of CT for both clinical purposes and lung cancer screening, the number of pulmonary nodules detected is expected to increase substantially. Despite well-established guidelines, many nodules do not receive proper evaluation due to a variety of factors, including inadequate coordination of care and financial and social barriers. To address this quality gap, novel approaches such as multidisciplinary nodule clinics and multidisciplinary boards may be necessary. As pulmonary nodules may indicate early-stage lung cancer, it is crucial to adopt a risk-stratified approach to identify potential lung cancers at an early stage, while minimizing the risk of harm and expense associated with over investigation of low-risk nodules. This article, authored by multiple specialists involved in nodule management, delves into the diagnostic approach to lung nodules. It covers the process of determining whether a patient requires tissue sampling or continued surveillance. Additionally, the article provides an in-depth examination of the various biopsy and therapeutic options available for malignant lung nodules. The article also emphasizes the significance of early detection in reducing lung cancer mortality, especially among high-risk populations. Furthermore, it addresses the creation of a comprehensive lung nodule program, which involves smoking cessation, lung cancer screening, and systematic evaluation and follow-up of both incidental and screen-detected nodules.

T2-fluid attenuated inversion recovery mismatch in tumefactive multiple sclerosis.

The T2-fluid attenuated inversion recovery (FLAIR) mismatch sign has been suggested as an imaging marker of isocitrate dehydrogenase-mutant 1p/19q non-codeleted gliomas with 100% specificity. Tumefactive demyelination is a common mimic of neoplasm that has led to unnecessary biopsies and even resections. We report a case of tumefactive multiple sclerosis in a 46-year-old male without prior symptomatic demyelinating episodes that demonstrates the T2-FLAIR mismatch sign. Our findings suggest the T2-FLAIR mismatch sign should not be used as a differential feature between glioma and tumefactive demyelination. Because typical isocitrate dehydrogenase-mutant 1p/19q non-codeleted gliomas typically do not demonstrate significant enhancement, such diagnosis should be reserved when post-contrast images are unavailable.

Generalizable machine learning for stress monitoring from wearable devices: A systematic literature review.

INTRODUCTION: Wearable sensors have shown promise as a non-intrusive method for collecting biomarkers that may correlate with levels of elevated stress. Stressors cause a variety of biological responses, and these physiological reactions can be measured using biomarkers including Heart Rate Variability (HRV), Electrodermal Activity (EDA) and Heart Rate (HR) that represent the stress response from the Hypothalamic-Pituitary-Adrenal (HPA) axis, the Autonomic Nervous System (ANS), and the immune system. While Cortisol response magnitude remains the gold standard indicator for stress assessment [1], recent advances in wearable technologies have resulted in the availability of a number of consumer devices capable of recording HRV, EDA and HR sensor biomarkers, amongst other signals. At the same time, researchers have been applying machine learning techniques to the recorded biomarkers in order to build models that may be able to predict elevated levels of stress. OBJECTIVE: The aim of this review is to provide an overview of machine learning techniques utilized in prior research with a specific focus on model generalization when using these public datasets as training data. We also shed light on the challenges and opportunities that machine learning-enabled stress monitoring and detection face. METHODS: This study reviewed published works contributing and/or using public datasets designed for detecting stress and their associated machine learning methods. The electronic databases of Google Scholar, Crossref, DOAJ and PubMed were searched for relevant articles and a total of 33 articles were identified and included in the final analysis. The reviewed works were synthesized into three categories of publicly available stress datasets, machine learning techniques applied using those, and future research directions. For the machine learning studies reviewed, we provide an analysis of their approach to results validation and model generalization. The quality assessment of the included studies was conducted in accordance with the IJMEDI checklist [2]. RESULTS: A number of public datasets were identified that are labeled for stress detection. These datasets were most commonly produced from sensor biomarker data recorded using the Empatica E4 device, a well-studied, medical-grade wrist-worn wearable that provides sensor biomarkers most notable to correlate with elevated levels of stress. Most of the reviewed datasets contain less than twenty-four hours of data, and the varied experimental conditions and labeling methodologies potentially limit their ability to generalize for unseen data. In addition, we discuss that previous works show shortcomings in areas such as their labeling protocols, lack of statistical power, validity of stress biomarkers, and model generalization ability. CONCLUSION: Health tracking and monitoring using wearable devices is growing in popularity, while the generalization of existing machine learning models still requires further study, and research in this area will continue to provide improvements as newer and more substantial datasets become available.

Role of Machine Learning-Based CT Body Composition in Risk Prediction and Prognostication: Current State and Future Directions.

CT body composition analysis has been shown to play an important role in predicting health and has the potential to improve patient outcomes if implemented clinically. Recent advances in artificial intelligence and machine learning have led to high speed and accuracy for extracting body composition metrics from CT scans. These may inform preoperative interventions and guide treatment planning. This review aims to discuss the clinical applications of CT body composition in clinical practice, as it moves towards widespread clinical implementation.

Rats and the city: Implications of urbanization on zoonotic disease risk in Southeast Asia.

Urbanization is rapidly transforming much of Southeast Asia, altering the structure and function of the landscape, as well as the frequency and intensity of the interactions between people, animals, and the environment. In this study, we explored the impact of urbanization on zoonotic disease risk by simultaneously characterizing changes in the ecology of animal reservoirs (rodents), ectoparasite vectors (ticks), and pathogens across a gradient of urbanization in Kuching, a city in Malaysian Borneo. We sampled 863 rodents across rural, developing, and urban locations and found that rodent species diversity decreased with increasing urbanization-from 10 species in the rural location to 4 in the rural location. Notably, two species appeared to thrive in urban areas, as follows: the invasive urban exploiter Rattus rattus (n = 375) and the native urban adapter Sundamys muelleri (n = 331). R. rattus was strongly associated with built infrastructure across the gradient and carried a high diversity of pathogens, including multihost zoonoses capable of environmental transmission (e.g., Leptospira spp.). In contrast, S. muelleri was restricted to green patches where it was found at high densities and was strongly associated with the presence of ticks, including the medically important genera Amblyomma, Haemaphysalis, and Ixodes. Our analyses reveal that zoonotic disease risk is elevated and heterogeneously distributed in urban environments and highlight the potential for targeted risk reduction through pest management and public health messaging.

Covalent Grafting of Polyoxometalate Hybrids onto Flat Silicon/Silicon Oxide: Insights from POMs Layers on Oxides.

Immobilization of polyoxometalates (POMs) onto oxides is relevant to many applications in the fields of catalysis, energy conversion/storage, or molecular electronics. Optimization and understanding the molecule/oxide interface is crucial to rationally improve the performance of the final molecular materials. We herein describe the synthesis and covalent grafting of POM hybrids with remote carboxylic acid functions onto flat Si/SiO2 substrates. Special attention has been paid to the characterization of the molecular layer and to the description of the POM anchoring mode at the oxide interface through the use of various characterization techniques, including ellipsometry, AFM, XPS, and FTIR. Finally, electron transport properties were probed in a vertical junction configuration and energy level diagrams have been drawn and discussed in relation with the POM molecular electronic features inferred from cyclic-voltammetry, UV-visible absorption spectra, and theoretical calculations. The electronic properties of these POM-based molecular junctions are driven by the POM LUMO (d-orbitals) whatever the nature of the tether or the anchoring group.