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Frontiers in Public Health 2024Uncertainty and inconsistency in terminology regarding the risk factors (RFs) for in-hospital falls are present in the literature. (Meta-Analysis)
Meta-Analysis
BACKGROUND
Uncertainty and inconsistency in terminology regarding the risk factors (RFs) for in-hospital falls are present in the literature.
OBJECTIVE
(1) To perform a literature review to identify the fall RFs among hospitalized adults; (2) to link the found RFs to the corresponding categories of international health classifications to reduce the heterogeneity of their definitions; (3) to perform a meta-analysis on the risk categories to identify the significant RFs; (4) to refine the final list of significant categories to avoid redundancies.
METHODS
Four databases were investigated. We included observational studies assessing patients who had experienced in-hospital falls. Two independent reviewers performed the inclusion and extrapolation process and evaluated the methodological quality of the included studies. RFs were grouped into categories according to three health classifications (ICF, ICD-10, and ATC). Meta-analyses were performed to obtain an overall pooled odds ratio for each RF. Finally, protective RFs or redundant RFs across different classifications were excluded.
RESULTS
Thirty-six articles were included in the meta-analysis. One thousand one hundred and eleven RFs were identified; 616 were linked to ICF classification, 450 to ICD-10, and 260 to ATC. The meta-analyses and subsequent refinement of the categories yielded 53 significant RFs. Overall, the initial number of RFs was reduced by about 21 times.
CONCLUSION
We identified 53 significant RF categories for in-hospital falls. These results provide proof of concept of the feasibility and validity of the proposed methodology. The list of significant RFs can be used as a template to build more accurate measurement instruments to predict in-hospital falls.
Topics: Accidental Falls; Humans; Risk Factors; Proof of Concept Study; Hospitalization
PubMed: 38932769
DOI: 10.3389/fpubh.2024.1390185 -
Sensors (Basel, Switzerland) Jun 2024The aim of the study is to compare the head displacement of the KPSIT C50 dummy during a frontal collision at a speed of 20 km/h, along with the change in the angle of...
The aim of the study is to compare the head displacement of the KPSIT C50 dummy during a frontal collision at a speed of 20 km/h, along with the change in the angle of the car seat backrest. Passenger car manufacturers recommend setting the backrest angle of the car seat between 100 and 125 degrees. It should be noted that the driver's position is of great importance in the event of a collision injury. In the event of a rear-end collision, the position of the headrest of the car seat is an element that affects the degree of the driver's injuries. In extreme cases, incorrect positioning of the headrest, even at low speed, can lead to serious injuries to the cervical spine and even death. The article is part of a large-scale study on low-speed crash testing. The research problem concerned the influence of the seat backrest angle on the head displacement during a low-speed collision. The article compares the displacement of the head of the KPSIT C50 dummy during a series of crash tests, where the angle of the car seat backrest was changed. On the basis of the research, it was found that the optimal angle of the car seat backrest is 110 degrees. In addition, a preliminary analysis of the displacements of the dummy's head showed a high risk of whiplash injury in people sitting in a fully reclined seat.
Topics: Humans; Accidents, Traffic; Head; Male; Automobiles; Manikins; Automobile Driving; Equipment Design
PubMed: 38931652
DOI: 10.3390/s24123868 -
Sensors (Basel, Switzerland) Jun 2024The latest survey results show an increase in accidents on the roads involving pedestrians and cyclists. The reasons for such situations are many, the fault actually...
The latest survey results show an increase in accidents on the roads involving pedestrians and cyclists. The reasons for such situations are many, the fault actually lies on both sides. Equipping vehicles, especially autonomous vehicles, with frequency-modulated continuous-wave (FMCW) radar and dedicated algorithms for analyzing signals in the time-frequency domain as well as algorithms for recognizing objects in radar imaging through deep neural networks can positively affect safety. This paper presents a method for recognizing and distinguishing a group of objects based on radar signatures of objects and a special convolutional neural network structure. The proposed approach is based on a database of radar signatures generated on pedestrian, cyclist, and car models in a Matlab environment. The obtained results of simulations and positive tests provide a basis for the application of the system in many sectors and areas of the economy. Innovative aspects of the work include the method of discriminating between multiple objects on a single radar signature, the dedicated architecture of the convolutional neural network, and the use of a method of generating a custom input database.
PubMed: 38931616
DOI: 10.3390/s24123832 -
SDC-Net++: End-to-End Crash Detection and Action Control for Self-Driving Car Deep-IoT-Based System.Sensors (Basel, Switzerland) Jun 2024Few prior works study self-driving cars by deep learning with IoT collaboration. SDC-Net, which is an end-to-end multitask self-driving car camera cocoon IoT-based...
Few prior works study self-driving cars by deep learning with IoT collaboration. SDC-Net, which is an end-to-end multitask self-driving car camera cocoon IoT-based system, is one of the research areas that tackles this direction. However, by design, SDC-Net is not able to identify the accident locations; it only classifies whether a scene is a crash scene or not. In this work, we introduce an enhanced design for the SDC-Net system by (1) replacing the classification network with a detection one, (2) adapting our benchmark dataset labels built on the CARLA simulator to include the vehicles' bounding boxes while keeping the same training, validation, and testing samples, and (3) modifying the shared information via IoT to include the accident location. We keep the same path planning and automatic emergency braking network, the digital automation platform, and the input representations to formulate the comparative study. The SDC-Net++ system is proposed to (1) output the relevant control actions, especially in case of accidents: accelerate, decelerate, maneuver, and brake, and (2) share the most critical information to the connected vehicles via IoT, especially the accident locations. A comparative study is also conducted between SDC-Net and SDC-Net++ with the same input representations: front camera only, panorama and bird's eye views, and with single-task networks, crash avoidance only, and multitask networks. The multitask network with a BEV input representation outperforms the nearest representation in precision, recall, f1-score, and accuracy by more than 15.134%, 12.046%, 13.593%, and 5%, respectively. The SDC-Net++ multitask network with BEV outperforms SDC-Net multitask with BEV in precision, recall, f1-score, accuracy, and average MSE by more than 2.201%, 2.8%, 2.505%, 2%, and 18.677%, respectively.
PubMed: 38931589
DOI: 10.3390/s24123805 -
Sensors (Basel, Switzerland) Jun 2024Driving while drowsy poses significant risks, including reduced cognitive function and the potential for accidents, which can lead to severe consequences such as trauma,...
Driving while drowsy poses significant risks, including reduced cognitive function and the potential for accidents, which can lead to severe consequences such as trauma, economic losses, injuries, or death. The use of artificial intelligence can enable effective detection of driver drowsiness, helping to prevent accidents and enhance driver performance. This research aims to address the crucial need for real-time and accurate drowsiness detection to mitigate the impact of fatigue-related accidents. Leveraging ultra-wideband radar data collected over five minutes, the dataset was segmented into one-minute chunks and transformed into grayscale images. Spatial features are retrieved from the images using a two-dimensional Convolutional Neural Network. Following that, these features were used to train and test multiple machine learning classifiers. The ensemble classifier RF-XGB-SVM, which combines Random Forest, XGBoost, and Support Vector Machine using a hard voting criterion, performed admirably with an accuracy of 96.6%. Additionally, the proposed approach was validated with a robust k-fold score of 97% and a standard deviation of 0.018, demonstrating significant results. The dataset is augmented using Generative Adversarial Networks, resulting in improved accuracies for all models. Among them, the RF-XGB-SVM model outperformed the rest with an accuracy score of 99.58%.
Topics: Humans; Radar; Artificial Intelligence; Neural Networks, Computer; Automobile Driving; Support Vector Machine; Algorithms; Machine Learning
PubMed: 38931541
DOI: 10.3390/s24123754 -
Sensors (Basel, Switzerland) Jun 2024Aircraft failures can result in the leakage of fuel, hydraulic oil, or other lubricants onto the runway during landing or taxiing. Damage to fuel tanks or oil lines...
Aircraft failures can result in the leakage of fuel, hydraulic oil, or other lubricants onto the runway during landing or taxiing. Damage to fuel tanks or oil lines during hard landings or accidents can also contribute to these spills. Further, improper maintenance or operational errors may leave oil traces on the runway before take-off or after landing. Identifying oil spills in airport runway videos is crucial to flight safety and accident investigation. Advanced image processing techniques can overcome the limitations of conventional RGB-based detection, which struggles to differentiate between oil spills and sewage due to similar coloration; given that oil and sewage have distinct spectral absorption patterns, precise detection can be performed based on multispectral images. In this study, we developed a method for spectrally enhancing RGB images of oil spills on airport runways to generate HSI images, facilitating oil spill detection in conventional RGB imagery. To this end, we employed the MST++ spectral reconstruction network model to effectively reconstruct RGB images into multispectral images, yielding improved accuracy in oil detection compared with other models. Additionally, we utilized the Fast R-CNN oil spill detection model, resulting in a 5% increase in Intersection over Union (IOU) for HSI images. Moreover, compared with RGB images, this approach significantly enhanced detection accuracy and completeness by 25.3% and 26.5%, respectively. These findings clearly demonstrate the superior precision and accuracy of HSI images based on spectral reconstruction in oil spill detection compared with traditional RGB images. With the spectral reconstruction technique, we can effectively make use of the spectral information inherent in oil spills, thereby enhancing detection accuracy. Future research could delve deeper into optimization techniques and conduct extensive validation in real airport environments. In conclusion, this spectral reconstruction-based technique for detecting oil spills on airport runways offers a novel and efficient approach that upholds both efficacy and accuracy. Its wide-scale implementation in airport operations holds great potential for improving aviation safety and environmental protection.
PubMed: 38931499
DOI: 10.3390/s24123716 -
Pharmaceuticals (Basel, Switzerland) Jun 2024Accidental poisonings by ingesting conjunctival fluid mixed with eye drops commonly involve alpha-2 adrenergic receptor agonists and tetrahydrozoline. These substances... (Review)
Review
Accidental poisonings by ingesting conjunctival fluid mixed with eye drops commonly involve alpha-2 adrenergic receptor agonists and tetrahydrozoline. These substances are recognized in commonly reported ingestions. Victims of all ages, otherwise in good health, often present as pale and lethargic to the emergency department (ED) after unintentionally ingesting topical eye medication. While eye drop poisoning cases in childhood include accidents during the play and poisonings in adults mean either suicide attempts or side effects caused by the systemic absorption of the substance, fluid of the ocular surface is a risk to all age groups. With this in mind, this study aimed to summarize data in the literature on tetrahydrozoline and alpha-2 adrenergic receptor agonists as dangerous medications, even when administered in low-bioavailability forms, such as eye drops. With this aim, a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-compliant systematic review of relevant studies was conducted. A search of PubMed, Scopus, Web of Science, and EBSCOhost yielded nine studies that met the rigorous inclusion criteria. The primary studies were subject to a meta-analysis once a quality appraisal of the studies and a narrative synthesis of the extracted data had been conducted. The author hopes that this information will provide observations that will lead to better designs for over-the-counter eye drops, off-label drug usage policies, and parental attention.
PubMed: 38931425
DOI: 10.3390/ph17060758 -
Micromachines May 2024The ever-growing prominence and widespread acceptance of organic light-emitting diodes (OLEDs), particularly those employing thermally activated delayed fluorescence... (Review)
Review
The ever-growing prominence and widespread acceptance of organic light-emitting diodes (OLEDs), particularly those employing thermally activated delayed fluorescence (TADF), have firmly established them as formidable contenders in the field of lighting technology. TADF enables achieving a 100% utilization rate and efficient luminescence through reverse intersystem crossing (RISC). However, the effectiveness of TADF-OLEDs is influenced by their high current density and limited device lifetime, which result in a significant reduction in efficiency. This comprehensive review introduces the TADF mechanism and provides a detailed overview of recent advancements in the development of host-free white OLEDs (WOLEDs) utilizing TADF. This review specifically scrutinizes advancements from three distinct perspectives: TADF fluorescence, TADF phosphorescence and all-TADF materials in host-free WOLEDs. By presenting the latest research findings, this review contributes to the understanding of the current state of host-free WOLEDs, employing TADF and underscoring promising avenues for future investigations. It aims to serve as a valuable resource for newcomers seeking an entry point into the field as well as for established members of the WOLEDs community, offering them insightful perspectives on imminent advancements.
PubMed: 38930673
DOI: 10.3390/mi15060703 -
Journal of Clinical Medicine Jun 2024Carotid stenosis (CS) is an atherosclerotic disease of the carotid artery that can lead to devastating cardiovascular outcomes such as stroke, disability, and death....
Predicting Major Adverse Carotid Cerebrovascular Events in Patients with Carotid Stenosis: Integrating a Panel of Plasma Protein Biomarkers and Clinical Features-A Pilot Study.
Carotid stenosis (CS) is an atherosclerotic disease of the carotid artery that can lead to devastating cardiovascular outcomes such as stroke, disability, and death. The currently available treatment for CS is medical management through risk reduction, including control of hypertension, diabetes, and/or hypercholesterolemia. Surgical interventions are currently suggested for patients with symptomatic disease with stenosis >50%, where patients have suffered from a carotid-related event such as a cerebrovascular accident, or asymptomatic disease with stenosis >60% if the long-term risk of death is <3%. There is a lack of current plasma protein biomarkers available to predict patients at risk of such adverse events. In this study, we investigated several growth factors and biomarkers of inflammation as potential biomarkers for adverse CS events such as stroke, need for surgical intervention, myocardial infarction, and cardiovascular-related death. In this pilot study, we use a support vector machine (SVM), random forest models, and the following four significantly elevated biomarkers: C-X-C Motif Chemokine Ligand 6 (CXCL6); Interleukin-2 (IL-2); Galectin-9; and angiopoietin-like protein (ANGPTL4). Our SVM model best predicted carotid cerebrovascular events with an area under the curve (AUC) of >0.8 and an accuracy of 0.88, demonstrating strong prognostic capability. : Our SVM model may be used for risk stratification of patients with CS to determine those who may benefit from surgical intervention.
PubMed: 38929911
DOI: 10.3390/jcm13123382 -
Medicina (Kaunas, Lithuania) May 2024: Healthcare facilities are complex systems due to the interaction between different factors (human, environmental, management, and technological). As complexity...
: Healthcare facilities are complex systems due to the interaction between different factors (human, environmental, management, and technological). As complexity increases, it is known that the possibility of error increases; therefore, it becomes essential to be able to analyze the processes that occur within these contexts to prevent their occurrence, which is the task of risk management. For this purpose, in this feasibility study, we chose to evaluate the application of a new safety walkaround (SWA) model. : A multidisciplinary working group made up of experts was established and then the subsequent phases of the activity were divided into three stages, namely the initial meeting, the operational phase, and the final meeting, to investigate knowledge regarding patient safety before and subsequently through visits to the department: the correct compilation of the medical record, adherence to evidence-based medicine (EBM) practices, the overall health and the degree of burnout of the various healthcare professionals, as well as the perception of empathy of staff by patients. : This working group chose to start this pilot project in the vascular surgery ward, demonstrating the ability of the tool used to capture the different aspects it set out to collect. In detail, the new version of SWA proposed in this work has made it possible to identify risk situations and system vulnerabilities that have allowed the introduction of corrective tools; detect adherence to existing company procedures, reschedule training on these specific topics after reviewing, and possibly update the same procedures; record the patient experience about the doctor-patient relationship and communication to hypothesize thematic courses on the subject; evaluate workers' perception of their health conditions about work, and above all reassure operators that their well-being is in the interest of the management of the healthcare company, which is maintained. Therefore, the outcome of the present study demonstrates the versatility and ever-present usefulness of the SWA tool.
Topics: Pilot Projects; Humans; Patient Safety; Feasibility Studies; Risk Management; Safety Management
PubMed: 38929520
DOI: 10.3390/medicina60060903