Robust Information Research Articles

Utilizing Federal Data Sources to Support Nursing Workforce Analysis.

Effective health workforce analysis requires robust data and information. Quality data facilitate monitoring workforce trends, identifying shortages, forecasting employment needs, and planning educational programs. A wide range of federal agencies collect various forms of data, including administrative data, surveys, and censuses, which can be used for health workforce analysis. However, identifying the most appropriate data sources to address a specific nursing workforce issue can be challenging, particularly for newcomers to the field or those researching unfamiliar topics. In this article, we introduce and review 18 federal data sources pertinent to nursing workforce analysis. We categorize the datasets by their associated federal agency, describe each source, discuss their applicability to nursing workforce studies, present examples of past studies that employed these datasets, and highlight their limitations. Our aim is to help researchers, policymakers, and healthcare administrators efficiently locate and leverage relevant data for their analysis.

A LiDAR SLAM Algorithm Considering Dynamic Extraction of Feature Points in Underground Coal Mine

Abstract. To address the absence of GNSS signals in underground coal mines and the susceptibility of mainstream LiDAR SLAM to degradation due to insufficient feature constraints, this paper proposes a tightly-coupled SLAM algorithm incorporating LiDAR and IMU for use in such environments. The paper initially introduces a dynamic feature point extraction strategy, where the number of corner feature points can be dynamically adjusted based on the occurrence of degradation in the underground coal mine environment. This approach constructs a constraint matrix with rich and robust feature information, enhancing pose estimation accuracy in environments with inadequate feature constraints, mitigating degradation effects, and reducing global cumulative error. Subsequently, the consistent building of the underground coal mine environment is achieved through back-end factor graph optimization. Finally, to validate the effectiveness of the method, experimental analysis is conducted in an underground coal mine. The results demonstrate that LeGO_LOAM exhibits poor robustness in underground coal mines and fails to construct a globally consistent pose estimation and map. Conversely, the pose estimation error of the proposed method in this paper is 50.93% lower in the plane direction and 42.13% lower in the elevation direction compared to LIO_SAM. This underscores the method's significance as crucial technical support for the intelligent perception and positioning of robots in underground coal mines.

Measurement of Hydraulic Fracture Aperture by Electromagnetic Induction.

We present a new method for accurately measuring the aperture of a fluid-driven fracture. This method uses an eddy current probe located within a completion tool specifically designed to obtain the fracture aperture in the wellbore at the location where the fluid is injected into the fracture. The probe induces an eddy current in a target object, producing a magnetic field that affects the overall magnetic field. It does not have any limitations with respect to fluid pressure and temperature within a large range, making it unlike other methods. We demonstrate the accuracy and performance of the sensor under laboratory conditions. A hydraulic fracture experiment in a porous sandstone is conducted and discussed. The obtained measurement of the evolution of the fracture inlet aperture by the eddy current probe during the multiple injection cycles performed provided robust information. The residual fracture aperture (after the test) measured by the probe is in line with estimations from image processing of X-ray CT scan images as well as a thin-section analysis of sub-parts of the fractured specimen. The robustness and accuracy of this electromagnetic induction probe demonstrated herein under laboratory conditions indicate an interesting potential for field deployment.

A study of short-term wind power segmentation forecasting method considering weather on ramp segments

The short-term fluctuation of wind power can affect its prediction accuracy. Thus, a short-term segmentation prediction method of wind power based on ramp segment division is proposed. A time-series trend extraction method based on moving average iteration is proposed on the full-time period to analyze the real-time change characteristics of power time-series initially; secondly, a ramp segment extraction method based on its definition and identification technique is proposed based on the results of the trend extraction; and a segmentation prediction scheme is proposed to lean the power prediction under different time-series: the LightGBM-LSTM is proposed for the non-ramping segment using point prediction, and the CNN-BiGRU-KDE is proposed for probabilistic prediction of ramp segments. From the results, this ramp segment definition and identification technique can effectively identify the ramp process of wind power, which makes up for the misidentification and omission of the classical climbing event definition; meanwhile, the segment prediction scheme not only meets the prediction accuracy requirements of the non-ramping segment, but also provides the effective robust information for the prediction of the ramping period, which offers reliable reference information for the actual wind farms. In particular, it is well adapted to wind power prediction under extreme working conditions caused by ramping weather, which is a useful addition to short-term wind power prediction research.

A Loose Integration of High-Rate GNSS and Strong-Motion Records with Variance Compensation Adaptive Kalman Filter for Broadband Co-Seismic Displacements

The loose integration system of high-rate GNSS and strong-motion records based on Kalman filtering technology is currently a research focus for capturing broadband co-seismic displacements. To address the problem of time-varying system noise variance in the standard Kalman filter (SKF), a variance compensation adaptive Kalman filter (VC-AKF) was adopted in this study to obtain more accurate high-precision broadband co-seismic displacement and provide reliable data support for seismic scientific research and practical applications. The algorithm continuously updates the system noise variance and calculates the state vector by collecting prediction residuals in real time. To verify the effectiveness and superiority of this method, a numerical simulation and a seismic experiment from the 2017 Ms 7.0 Jiuzhaigou earthquake were carried out for comparative analysis. Based on the simulation results, the precision of the proposed algorithm was 46% higher than that of the SKF. The seismic experiment results indicate that the proposed VC-AKF approach can eliminate the baseline shift of accelerometers and weaken the influence of time-varying system noise variance towards more robust displacement information.

Koalas, friends and foes—The application of airborne eDNA for the biomonitoring of threatened species

Abstract Curbing global wildlife population declines will necessitate the protection of their habitat, and subsequent robust baseline information about wildlife that use and occupy it. Collating such information, however, remains a challenging and costly endeavour. Little did we know that traces of wildlife presence float in the air and can be detected through traces of DNA. Here, we deployed samplers to test the applicability of collecting airborne eDNA for the detection of a threatened species, the koala, and its co‐occurring terrestrial mammalian community. We develop a novel species specific qPCR assay to detect the presence of koalas and applied this in concert with a meta‐barcoding approach to detect the co‐occurring mammalian community. Through sampling of airborne particles, we successfully detected koala presence accurately to habitat patch level, alongside 16 unique taxonomic assignments, successfully assigning 11 of these to species level including detections belonging to the wallaby, antechinus, members of possum family and invasive species such as foxes, domestic dogs and hares. Synthesis and applications: We demonstrate the potential of airborne eDNA for the detection of threatened terrestrial wildlife and their surrounding ecological community under natural conditions. With achievable optimisations we detail how airborne eDNA may be applied for the management and monitoring of threated species for enhanced conservation efforts.

3D Head Pose Estimation via Normal Maps: A Generalized Solution for Depth Image, Point Cloud, and Mesh

Head pose estimation plays a crucial role in various applications, including human–machine interaction, autonomous driving systems, and 3D reconstruction. Current methods address the problem primarily from a 2D perspective, which limits the efficient utilization of 3D information. Herein, a novel approach, called pose orientation‐aware network (POANet), which leverages normal maps for orientation information embedding, providing abundant and robust head pose information, is introduced. POANet incorporates the axial signal perception module and the rotation matrix perception module, these lightweight modules make the approach achieve state‐of‐the‐art (SOTA) performance with few computational costs. This method can directly analyze various topological 3D data without extensive preprocessing. For depth images, POANet outperforms existing methods on the Biwi Kinect head pose dataset, reducing the mean absolute error (MAE) by ≈30% compared to the SOTA methods. POANet is the first method to perform rigid head registration in a landmark‐free manner. It also incorporates few‐shot learning capabilities and achieves an MAE of about on the Headspace dataset. These features make POANet a superior alternative to traditional generalized Procrustes analysis for mesh data processing, offering enhanced convenience for human phenotype studies.

Occupational Hazards Among the Agricultural Workers in a Rural Area of South India

Background: Agriculture is one of the most hazardous occupations, employing around 60% of India’s workforce. Agricultural workers face occupational hazards in the form of physical, chemical, biological, mechanical, ergonomic, psychosocial, and so on. This study assessed the physical, chemical, biological, and mechanical health hazards among agricultural workers.Methods: A cross-sectional study was conducted among 400 agricultural workers selected through multistage random sampling in a rural area to assess their occupational hazards.Results: Physical hazards were the most common hazards reported by 390 workers (97.5%), followed by mechanical hazards by 200(50%), and biological hazards by 79(19.7%). Chemical hazards were noted in 136(86.4%) out of 155 participants who used chemicals in the form of pesticides and insecticides.Conclusion: All agricultural workers experience one or the other types of occupational hazards. These hazards have a bearing on the quality of life among the agricultural workers who form the primary workforce in our country and contribute significantly to the economy. It is crucial to ensure a robust health information system to track down and respond to agricultural workers’ various health events, develop and implement a need-based intervention program through the formal healthcare system, and prevent and avert occupational health hazards in this group.

Tumor budding in pre-neoadjuvant biopsy and post-neoadjuvant resection specimens is associated with poor prognosis in intrahepatic cholangiocarcinoma—a cohort study of 147 cases by modified ITBCC criteria

Tumor budding (TB) has been associated with poor survival in a variety of cancers including intrahepatic cholangiocarcinoma (iCCA). As tumor histomorphological features are significantly altered after neoadjuvant therapy (NAT), our study aims to assess the prognostic significance of TB in iCCA patients before and after NAT, by the modified International Tumor Budding Consensus Conference (ITBCC) criteria. 147 NAT-treated iCCA cases were included in this study. In biopsy specimens obtained before NAT, the TB-positive subgroup had lower overall survival (OS) in univariate analysis (P = 0.010). In resection specimens obtained after NAT, the TB-positive subgroup had reduced OS (P = 0.002) and recurrence-free survival (RFS) (P = 0.013) in univariate analysis. In multivariate analysis including TNM stage, lymphovascular invasion and perineural invasion, TB-positive in post-NAT resection was also found as an independent prognostic factor for both OS and RFS (OS, HR, 3.005; 95% CI, 1.333–6.775, P = 0.008; RFS, HR, 1.748; 95% CI, 1.085–2.816, P = 0.022). In conclusion, assessing the presence of TB by modified ITBCC criteria provides robust prognostic information in the NAT setting of iCCA patients and can be considered to be included in routine pathological reporting.

An exploration of teacher and school-based nurse perceptions of current HPV education offered to students 15-16 years old in post-primary schools in Northern Ireland, UK.

Human papillomavirus virus (HPV) is highest among young adults 15-24 years old. High-risk strains are responsible for the development of cancers including cervical, vaginal, vulvar, anal, oropharyngeal and penile. Despite HPV school-based vaccination programmes in the UK, HPV vaccination uptake rates continue to fluctuate due to misinformation and vaccine hesitancy post COVID-19. The aim of this study is to explore perceptions of post-primary school teachers and nurses regarding the current HPV education provision and the need to provide HPV education to students 15-17 years old when they are most likely to be becoming sexually active. A qualitative study was conducted using online semi-structured interviews between February-August 2022 with post-primary teachers and nurses in Northern Ireland, UK. Stratified random sampling was used to contact schools to recruit participants. Recruitment continued until data saturation was reached. Braun & Clarke's six-phase framework for reflexive thematic analysis was used to analyse the data. Twelve teachers and six nurses participated in the study. Four themes arose based on the analysis including 1) the importance of HPV education 2) self-consent to the HPV vaccine 3) design of the HPV education and 4) delivery of the HPV education. Identified barriers to implementation of HPV education included lack of parental education, religion and the conservative culture of Northern Ireland. Participants perceived HPV education to be poor or non-existent in their schools but placed high importance on this education. They indicated that a non-judgemental health professional would be the ideal person to deliver interactive HPV education as part of a mandated spiral curriculum. HPV education at 15-17 years old provides students with an opportunity to learn about their HPV risk, their HPV vaccination status and an opportunity to self-consent to the HPV vaccine. The Education Authority and Department of Health should support health professionals to deliver consistent robust HPV health information to students of this age.

Inflated effect estimates for vitamin D supplementation are driven by common meta-analytical errors.

Han et al. (J Int Soc Sports Nutr 16:55, 2019) sought to quantify the effects of vitamin D supplementation on strength outcomes among athletes in a meta-analysis. The authors reported a pooled effect size (standardized mean difference; SMD) of -0.75 (95% CI: -1.82 to 0.32, p = 0.17) in favor of supplementation, but the analytical approach was not appropriate for a pooled analysis of randomized controlled trials and the effect sizes were calculated incorrectly. This letter discusses how these issues impact the results and interpretation of the paper, then provides an update on the estimated average effect of vitamin D on strength outcomes in athletes. Identified errors included the use of within-group rather than between-group effect size metrics, the use of standard error values in place of standard deviations, and failure to account for correlated observations within the model. The data were reanalyzed after correcting for these common meta-analytic errors. The results of this reanalysis reflect a dramatically smaller and statistically nonsignificant pooled effect estimate of SMD = 0.16 (-0.24 to 0.56, p = 0.43) in favor of supplementation. Further, the model from this reanalysis has more favorable statistical characteristics than the original analysis, as evidenced by a fairly symmetrical funnel plot and a nonsignificant result for Cochrane's Q test (Q = 5.02, p = 0.41). In order to disseminate robust information to sports nutrition practitioners and researchers, it is critically important for meta-analyses to produce valid effect estimates that are appropriate for the underlying study designs and calculated without error. This letter highlights common errors to inform the calculation and interpretation of future meta-analyses in sports nutrition.

Lightweight medical image segmentation network with multi-scale feature-guided fusion

In the field of computer-aided medical diagnosis, it is crucial to adapt medical image segmentation to limited computing resources. There is tremendous value in developing accurate, real-time vision processing models that require minimal computational resources. When building lightweight models, there is always a trade-off between computational cost and segmentation performance. Performance often suffers when applying models to meet resource-constrained scenarios characterized by computation, memory, or storage constraints. This remains an ongoing challenge. This paper proposes a lightweight network for medical image segmentation. It introduces a lightweight transformer, proposes a simplified core feature extraction network to capture more semantic information, and builds a multi-scale feature interaction guidance framework. The fusion module embedded in this framework is designed to address spatial and channel complexities. Through the multi-scale feature interaction guidance framework and fusion module, the proposed network achieves robust semantic information extraction from low-resolution feature maps and rich spatial information retrieval from high-resolution feature maps while ensuring segmentation performance. This significantly reduces the parameter requirements for maintaining deep features within the network, resulting in faster inference and reduced floating-point operations (FLOPs) and parameter counts. Experimental results on ISIC2017 and ISIC2018 datasets confirm the effectiveness of the proposed network in medical image segmentation tasks. For instance, on the ISIC2017 dataset, the proposed network achieved a segmentation accuracy of 82.33 % mIoU, and a speed of 71.26 FPS on 256 × 256 images using a GeForce GTX 3090 GPU. Furthermore, the proposed network is tremendously lightweight, containing only 0.524M parameters. The corresponding source codes are available at https://github.com/CurbUni/LMIS-lightweight-network.

Associations of 2 Established Methods of Measuring Gait Speed and Sit-To-Stand Performance with Frailty and Life-Space Mobility in Community-Dwelling Older Adults

ObjectivesThe 4-m gait speed (4mGS) and 10-m gait speed (10mGS) tests and the 30-second sit-to-stand (30sSTS) and 5-times sit-to-stand (5xSTS) tests are commonly used and advocated in consensus recommendations. We compared these tests on their predictive and clinical value concerning the risk of prefrailty/frailty and restricted life-space mobility (RLSM). DesignCross-sectional study. Setting and ParticipantsA sample of 1235 community-dwelling adults (mean ± SD, 68 ± 7 years) participated in this prospective cohort study. MethodsAt baseline assessment, participants completed a survey and functional assessment, from which gait speed, sit-to-stand performance, self-reported mobility limitation, 40-item Frailty Index, and Life Space Assessment were measured. Participants with a 40-item Frailty Index >0.15 and a Life Space Assessment <60 points were classified as having prefrailty/frailty and RLSM, respectively. At 1-year follow-up assessment, prefrailty/frailty and RLSM were evaluated. ResultsCorrelations between gait speed and sit-to-stand measures were high (ρ values >0.80). In multivariable ordinal models, these measures added incremental prognostic value beyond a base model comprising demographics and self-reported mobility limitation variables in predicting baseline and 1-year outcomes. Between 10mGS and 4mGS, models with 10mGS had higher concordance indices (differences, 0.005-0.009), and these differences translated to generally greater net benefit in decision curve analyses. Between 30sSTS and 5xSTS measures, no one measure consistently outperformed the other, with small net benefit differences between measures (<0.2%). Conclusions and ImplicationsIn community-dwelling older adults, gait speed and sit-to-stand measures meaningfully predicted prefrailty/frailty and RLSM. 10mGS provided more robust prognostic information than the 4mGS, whereas 5xSTS and 30sSTS measures showed near equivalence of performance. These findings could guide the choice of functional measures in clinical and research settings.

Deciphering laser shock peening quality monitoring: Wavelet-driven network with interpretability

Quality monitoring of laser shock peening based on acoustic emission technology is a topical multidisciplinary issue that has received much attention in recent years. To acquire complex time-varying information in acoustic emission signals, convolutional neural networks with powerful learning capabilities have shown potential for a wide range of applications. However, the black-box property of the network imposes limitations on its further development and decision credibility. Therefore, this study proposes a wavelet-driven network with theoretical basis and physical significance. This network can cyclically utilize discrete wavelet packet transform to map input features to the wavelet domain during the learning process, thereby obtaining more robust and valuable information. This paper also constructs a novel wavelet attention mechanism that takes into account the difference between low-frequency and high-frequency information, and is able to allocate resources in both the decomposition component and the time-domain dimension. The proposed method can be seen as a multiresolution analysis technique that combines existing physical knowledge with nonlinear feature processing and feature selective enhancement. The results of the two laser shock peening cases show that the proposed method not only outperforms current state-of-the-art models in terms of monitoring performance, but also has better physical interpretability. Importantly, the proposed method has the potential to be further extended to other interpretable structural health monitoring.

Design of an emergency medical information system for mass gatherings

BackgroundThe frequency of mass gatherings is increasing. Such events often involve many people and carry the risk of mass casualty incidents, which require substantial medical resources from various healthcare institutions. The current medical system, while meeting daily needs, struggles to address the demand for a high volume of emergency resources and real-time data exchange. ObjectiveThe aim of this study was to develop an emergency medical information system for mass gatherings. MethodsWe developed an emergency medical information system for mass gatherings. Based on a unified prehospital and intrahospital emergency data exchange protocol, we can directly standardize medical information data and provide data support for the evacuation decision support algorithms of multiple institutions. Wearable devices, vehicle-mounted devices, video calling systems and surveillance systems are connected to capture real-time scenes. ResultsWe constructed the system via mobile applications and online platforms and deployed it in 3 hospitals, 5 ambulances and 17 on-site medical locations. We constructed a set of electronic medical records covering the whole first aid process according to the basic principles of first aid. The simulation results show that the proposed algorithm is suitable for mass gatherings. The overall survival rate of patients can be improved by 5 %, and the average evacuation efficiency of patients can be improved by 50 %. Furthermore, in a real-world environment, this method can ensure patient survival and achieve good convergence. ConclusionsOur system is capable of providing robust medical information support for emergency medical services during large-scale assembly events, ensuring a visualized full-process emergency response and decision-making for the diversion and subsequent transport of a large patient population.

How Are Weather and Climate Products and Decision Support Systems Used in Wildland Fire Decision-Making in the U.S. Southwest?

Abstract Wildland fire management is a challenging environment in which to utilize scientific information. Despite the robust information system that supports U.S. wildland fire management, recent studies demonstrate that some data/information products and decision support tools available to that community are not being used to their full potential. In the U.S. Southwest, the convergence of climate change, past wildland fire policy, and an expanding wildland–urban interface have increased pressure on wildland fire managers to use science-based information to improve management outcomes. This tension between the perceived need for more science in fire management and the practical challenges of using science to inform decisions led us to ask two basic questions: 1) How is information about weather and climate used in wildland fire management decision-making in the Southwest? 2) How are decision support tools used in Southwest wildland fire management? We found that relatively few sources of weather and climate information are consulted by Southwest wildland fire managers; a small percentage of those sources are used to inform the specific decisions and tasks we asked about; and decision support tools are commonly used to justify rather than inform decisions. We further found evidence that suggests that products and tools that are thoughtfully designed to fit within the specific information use environment of wildland fire management—rather than being produced in isolation from that environment—are more likely to be used and therefore have more impact than those that are produced in isolation from that environment. Significance Statement This study set out to understand how weather and climate information and decision support systems (DSSs) are perceived and used by the Southwest wildland fire management community. We found that the wildland fire community consults relatively few weather and climate products, even fewer are used to inform decisions, and that DSSs are frequently used to justify rather than inform decisions. We argue that the climate services community and other scientists who develop science-based tools and products for fire management will benefit from better understanding the information use environment in which their science may be used if they want to increase the utility of their work.

Fault Line Selection of Small Current Grounding System Research Based on Zero Sequence Circuit Current Phase

Abstract This paper investigates fault line selection in small current grounding systems, focusing on the zero sequence current phase characteristics. Small current grounding systems, prevalent in medium voltage distribution networks, face challenges in fault detection due to minimal fault currents during single status phase grounding faults. Traditional methods relying on manual line switching and zero sequence voltage monitoring are inefficient and can induce overvoltage. This study categorizes fault line selection techniques into three approaches: steady-state fault information, transient information, and modern signal processing technology. The analysis of zero sequence currents in non-grounded systems reveals that fault line zero sequence currents equal the sum of system capacitive currents, flowing opposite to those in non-fault lines. Additionally, transient signal analysis shows that these signals provide more robust information for fault detection. MATLAB simulations validate the theoretical models, demonstrating that zero sequence current direction and amplitude effectively identify fault lines. The results confirm the efficacy of combining steady-state and transient signal analyses for accurate fault line selection, contributing to enhanced safety and reliability in power distribution networks. This research offers practical insights and tools for engineers, improving fault management and reducing potential economic losses in small current grounding systems.

Identification of Flood-Prone Areas Using Geo-Informatics: A Case Study of Erbil City, Kurdistan Region, Iraq

Global flood events, intensified by an intricate interplay of natural dynamics and human activities, pose severe environmental and safety threats, driving the demand for advanced vulnerability assessments. This research innovates with a holistic geo-informatics methodology, pinpointing flood-prone regions in Erbil City, Kurdistan Region, Iraq. It harnesses the Analytic Hierarchy Process and Geographic Information System synergies, enriched with insights from multispectral satellite observations, to conduct an exhaustive analysis of key flood susceptibility indicators. These encompass geological, hydrological, meteorological, and anthropogenic parameters, integrated into a robust Geographic Information System analytical matrix. Through strategic factor analysis and prioritization, the study distills a detailed flood susceptibility cartogram, classifying regional vulnerabilities into distinct risk echelons: high (encompassing 25%), moderate (38.1%), low (28.4%), and very low (8.5%). The final flood map highlights an acute susceptibility epicenter in the northeast, contrasting with the minimal threat southwest. This demarcation reaffirms the Analytic Hierarchy Process-Geographic Information System fusion's precision in translating multifaceted data into strategic intelligence, proving instrumental for scholars, administrative frameworks, and developmental strategists. The insights garnered are pivotal in sculpting informed urban development and flood counteractive blueprints, instrumental in mitigating flood repercussions and preserving ecological and human integrities.

Quality of patient Care with new Privatized Healthcare system: A Systematic Review of Technology Integration and Health Insurance"

Background: The quality of patient care is effective for new privatized healthcare system. For providing the effective services to the patients’ technology tools play important role. Also, new privatized healthcare organizations introduce the healthcare insurance. The aim of current systematic review is to explore the quality of patient care with new privatized healthcare system in the context of technology integration and health insurance. Method: A thorough search of databases, including Scopus, PsycINFO, and Web of Science, was conducted in order to categorize relevant research that was published between 2020 and 2024. The inclusion criteria for this research were English-language papers that focused on exploring the quality of patient care with new privatized healthcare system in the context of technology integration and health insurance.Following an initial screening and quality evaluation, eleven studies were included in the synthesis. Results: The study database was searched through electronic databases, identifying 1679 records. 15 unique records were assessed for eligibility based on titles and abstracts. After initial screening, 11 studies were selected for full-text assessment. After independent review, 11 studies met criteria and were included in the systematic review. The selected studies were conducted between 2020-2024 and varied in design. The PRISMA flowchart illustrates the selection process. Quality evaluation involves peer-reviewed journals, overall assessment, and quality management. Conclusion: As the SR concluded that advanced technologies like electronic health records, telemedicine, and predictive analytics can improve patient care and treatment accuracy. However, challenges like rising costs, complex insurance plans, and data security need to be addressed. Effective training for healthcare providers, clear insurance plans, and robust information security systems are crucial for maximizing profits.

Injection Techniques to Reduce Adverse Effects of Subcutaneous Low-Molecular-Weight Heparin Among Patients With Cardiovascular Diseases: A Scoping Review.

To systematically review the existing literature and address the following research question: What are the most effective techniques used to minimise adverse effects resulting from subcutaneous injections of low-molecular-weight heparin among patients with cardiovascular diseases? A scoping review. A comprehensive search was conducted across multiple databases, including CINAHL, PubMed, EMBASE and the Cochrane Library, from 1 February 2014 to 31 January 2024. Participants were aged 18 years or older, diagnosed with venous thromboembolism or arterial thromboembolism and had prescribed subcutaneous injections of low-molecular-weight heparin. The collected data were analysed following the Joanna Briggs Institute approach, and it was organised and categorised based on the main objectives of the review. Twenty studies were eligible, including 1 best practice project, 7 randomised controlled trials and 9 quasi-experimental studies. The techniques under investigation encompassed various aspects, including the injection site, injection duration (e.g., 30 s vs. 10 s), injection method (e.g., needle insertion angle), duration of needle withdrawal after injection, pressure application time and cold pressure. Preliminary evidence suggests that techniques such as using the abdominal site and slower injection rates may help reduce adverse effects. However, the optimal parameters for injection duration, waiting time, pressure and cold application, including the duration of these applications, remain uncertain due to limitations in sample size and heterogeneity in interventions and outcome measures across the studies. Ensuring the accurate administration of low-molecular-weight heparin is of utmost importance as it plays a critical role in decreasing mortality rates and minimising substantial healthcare costs linked to complications arising from incorrect administration. The findings from the current review have significantly contributed to strengthening the evidence base in this field, providing more robust and reliable information. This review emphasises the significance of implementing standardised subcutaneous injection techniques for low-molecular-weight heparin in patients with cardiovascular disease in order to reduce complications and enhance patient outcomes. This study followed the applicable guidelines established by the PRISMA 2020 statement. The PRISMA checklist for systematic reviews was utilised for reporting purposes. There is no patient or public contribution to declare. OSF registries: osf.io/phk72.