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46,151 results for “Safety”
Quality and Utility of European Cardiovascular and Orthopaedic Registries for the Regulatory Evaluation of Medical Device Safety and Performance Across the Implant Lifecycle: A Systematic Review - Dataset
<p><strong>Background: </strong>The European Union Medical Device Regulation (MDR) requires manufacturers to undertake post-market clinical follow-up (PMCF) to assess the safety and performance of their devices following approval and Conformité Européenne (CE) marking. The quality and reliability of device registries for this Regulation have not been reported. As part of the Coordinating Research and Evidence for Medical Devices (CORE-MD) project, we identified and reviewed European cardiovascular and orthopaedic registries to assess their structures, methods, and suitability as data sources for regulatory purposes.</p> <p><strong>Methods: </strong>Regional, national and multi-country European cardiovascular (coronary stents and valve repair/replacement) and orthopaedic (hip/knee prostheses) registries were identified using a systematic literature search. Annual reports, peer-reviewed publications, and websites were reviewed to extract publicly available information for 33 items related to structure and methodology in six domains and also for reported outcomes.</p> <p><strong>Results: </strong>Of the 20 cardiovascular and 26 orthopaedic registries fulfilling eligibility criteria, a median of 33% (IQR: 14%-71%) items for cardiovascular and 60% (IQR: 28%-100%) items for orthopaedic registries were reported, with large variation across domains. For instance, no cardiovascular and 16 (62%) orthopaedic registries reported patient/ procedure-level completeness. No cardiovascular and 5 (19%) orthopaedic registries reported outlier performances of devices, but each with a different outlier definition. There was large heterogeneity in reporting on items, outcomes, definitions of outcomes, and follow-up durations.</p> <p><strong>Conclusion: </strong>European cardiovascular and orthopaedic device registries could improve their potential as data sources for regulatory purposes by reaching consensus on standardised reporting of structural and methodological characteristics to judge the quality of the evidence as well as outcomes.</p>
Negative allometry of leaf xylem conduit diameter and double-wall thickness: implications for implosion safety
<p>Xylem conduits have lignified walls to resist crushing pressures. The thicker the double-wall (<em>T</em>) relative to its diameter (<em>D</em>), the greater the implosion safety. Having safer conduits may incur higher costs and reduced flow, while having less resistant xylem may lead to catastrophic collapse under drought. Although recent studies have shown that conduit implosion commonly occurs in leaves, little is known about how leaf xylem scales <em>T</em> versus <em>D</em> to trade off safety, flow efficiency, mechanical support, and cost.</p> <p>We measured <em>T</em> and <em>D</em> in > 7,000 conduits of 122 species to investigate how <em>T</em> versus <em>D</em> scaling varies across clades, habitats, growth forms, leaf and vein sizes.</p> <p>As conduits become wider, their double-cell walls become proportionally thinner, resulting in a negative allometry between <em>T</em> and <em>D</em>. That is, narrower conduits, which are usually subjected to more negative pressures, are proportionally safer than wider ones. Higher implosion safety (i.e higher <em>T/D</em> ratios) was found in asterids, arid habitats, shrubs, small leaves, and minor veins.</p> <p>Despite the strong allometry, implosion safety does not clearly trade off with other measured leaf functions, suggesting that implosion safety at whole-leaf level cannot be easily predicted solely by individual conduits' anatomy.</p>
Technical and user evaluation of mobile application for pedestrian safety
<p>This file contains the evaluation results of the tests carried out a Versailles during the Show project with external participants:</p> <ul> <li>Technical indicators have been collected in one sheet</li> <li>Feedbacks from the user questionnaires have been collected in one other sheet</li> </ul>
Raw NMR and GC reports for the article Electrochemical Hydrogenation of Alkenes over a Nickel Foam Guided by Life Cycle, Safety and Toxicological Assessments, Green Chemistry, doi: https://doi.org/10.1039/D4GC02924K
<p>Raw NMR for the article Electrochemical Hydrogenation of Alkenes over a Nickel Foam Guided by Life Cycle, Safety and Toxicological Assessments, Green Chemistry, doi: https://doi.org/10.1039/D4GC02924K</p> <p>The raw NMR data files for all compounds reported in the article are included. The numbering corresponds to those in the article.</p> <p>Each parent folder contains subfolders with different files. In order to process this data, the full parent folder must be dragged into either Mestrenova or Topspin and then the data is automatically processed. If the name of the raw data files are renamed, the software (<a href="https://mestrelab.com/" target="_blank" rel="noopener noreferrer">Mestrenova</a> or <a href="https://www.bruker.com/en/products-and-solutions/mr/nmr-software/topspin.html" target="_blank" rel="noopener noreferrer">Topspin</a>) will not be able to process the files</p> <p> </p>
Attitudes of Staff Involved in Dental Radiological Procedures in Georgia Towards Radiation Protection and Safety: A Questionnaire-Based Study
<p>Objectives: To investigate dentist’s perceptions and attitudes regarding radiation safety in dental practices in Georgia.</p> <p>Methods: More than 100 dentists involved in dental X-ray diagnostic procedures across the country participated in this study. The questionnaire was submitted via Google form, a total of 17 key questions with relevant sub-questions. Further data were evaluated according to the frequency distribution. An anonymous survey was conducted regarding the following areas: years of practice, types and characteristics of the radiography equipment, knowledge of BSS and national regulations, prescription of dental radiographs, use of radiation protection equipment, participation in the radiation safety training program and etc.</p> <p>Results: The obtained data are presented in the form of tables and diagrams. Analysis of the answers showed a fairly high level of knowledge and responsibility for radiation safety among all survey participants. A certain difference in the answers depends both on work experience, education and on a number of other factors, for the study of which additional research is needed.</p> <p>Conclusions: This article is the first study in Georgia conducted jointly by a scientific organization, a national regulatory authority, as well as educational and professional structures. The analysis of the results of the presented questionnaire, on the one hand, allows the assessment of mandatory knowledge of radiation protection and safety issues of the target personnel. On the other hand, such feedback based on statistical data is a prerequisite for the revision of possible further regulatory requirements. Staff awareness issues involve the involvement of the relevant professional associations, which will allow it to strengthen the requirements for its members in the long run, as well as better analyze existing practices.</p>
Data for a mapping study about the usage of MDE in Safety and Security Domain
<p>Data for a mapping study. Contains a listing of papers and information about their content.</p>
A scoping review of patient safety research carried out in the Republic of Ireland [dataset]
<p>This is an example search strategy (supplemental materials 1) and the charting form data (supplemental materials 2) from each of the 31 included papers included in the scoping research.</p> <p><strong>Background.</strong> Maintaining the highest levels of patient safety is a priority of healthcare organisations. However, although considerable resources are invested in improving safety, patients still suffer avoidable harm.</p> <p><strong>Aims.</strong> (1) To examine the extent, range, and nature of patient safety research activities carried out in the RoI; (2) make recommendations for future research; and (3) consider how these recommendations align with the Health Service Executive’s (HSE) patient safety strategy.</p> <p><strong>Methods.</strong> A five-stage scoping review methodology was used to synthesise the published research literature on patient safety carried out in the RoI: (1) identify the research question; (2) identify relevant studies; (3) study selection; (4) chart the data; and (5) collate, summarising, and reporting the results. Electronic searches were conducted across five electronic databases.</p> <p><strong>Results.</strong> A total of 31 papers met the inclusion criteria. Of the 24 papers concerned with measuring and monitoring safety, 12 (50%) assessed past harm, 4 (16.7%) the reliability of safety systems, 4 (16.7%) sensitivity to operations, 9 (37.5%) anticipation and preparedness, and 2 (8.3%) integration and learning. Of the six intervention papers, three (50%) were concerned with education and training, two (33.3%) with simplification and standardisation, and one (16.7%) with checklists. One paper was concerned with identifying potential safety interventions.</p> <p><strong>Conclusions.</strong> There is a modest, but growing, body of patient safety research conducted in the RoI. It is hoped that this review will provide direction to researchers, healthcare practitioners, and health service managers, in how to build upon existing research in order to improve patient safety.</p>
Dataset of A Review on the Efficacy, and Safety of nab-paclitaxel with Gemcitabine in Combination with other Drugs
<p>Dataset of A Review on the Efficacy, and Safety of nab-paclitaxel with Gemcitabine in Combination with other Drugs as New Therapeutic Strategies in Pancreatic Cancer</p> <p><strong>Table 1.</strong> Patient characteristics.</p> <p><strong>Table 2.</strong> The Joanna Briggs Institute Critical Appraisal Checklist</p> <p><strong>Table 3.</strong> The Joanna Briggs Institute critical appraisal checklist for quasi-experimental studies (non-randomized experimental studies)</p> <p><strong>Table 4</strong>. Comparison of response to treatment</p> <p><strong>Table 5.</strong> Summary of all grades Adverse Events (AE) in treatments that include placebo</p> <p><strong>Table 6.</strong> Summary of all grades Adverse Events (AE) in treatments that include no placebo</p>
Dataset related to article "Intracranial Sonodynamic Therapy With 5-Aminolevulinic Acid and Sodium Fluorescein Safety Study in a Porcine Model"
<p>pigs treatments</p>
High variation in hydraulic efficiency but not xylem safety between roots and branches in four temperate broad-leaved tree species
<p>Xylem hydraulic safety and efficiency are key traits determining tree fitness in a warmer and drier world. While numerous plant hydraulic studies have focused on branches, our understanding of root hydraulic functioning remains limited, although roots control water uptake, influence stomatal regulation and have commonly been considered as the most vulnerable organ along the hydraulic pathway. We investigated 11 traits related to xylem safety and efficiency along the hydraulic pathway in four temperate broad-leaved tree species. Continuous vessel tapering from coarse roots to stems and branches caused considerable reduction in hydraulic efficiency. Wood density was always lowest in roots, but did not decline linearly along the flow path. In contrast, xylem embolism resistance (P50) did not differ significantly between roots and branches, except for one species. The limited variation in xylem safety between organs did not adequately reflect the corresponding reductions in vessel diameter (by ~70%) and hydraulic efficiency (by ~85%). Although we did not observe any trade-off between xylem safety and specific conductivity, vessel diameter, vessel lumen fraction and wood density were related to embolism resistance, both across and partly within organs. We conclude that coarse roots are not highly vulnerable to xylem embolism as commonly believed, indicating that hydraulic failure during soil drying might be restricted to fine roots.</p>
Chinese Chemical Safety Signs (CCSS)
<p><strong>Chinese Chemical Safety Signs (CCSS)</strong></p> <p>This dataset is compiled as a benchmark for recognizing chemical safety signs from images. We provide both the dataset and the experimental results.</p> <p><strong><em>1. The Dataset</em></strong></p> <p>The complete dataset is contained in the folder <code>ccss/data</code>. The images include signs based on the Chinese standard "Safety Signs and their Application Guidelines" (GB 2894-2008) for safety signs in chemical environments. This standard, in turn, refers to the standards ISO 7010 (Graphical symbols – Safety Colours and Safety Signs – Safety signs used in workplaces and public areas), GB/T 10001 (Public Information Graphic Symbols for Signs), and GB 13495 (Fire Safety Signs)</p> <p><strong>1.1. Image Collection</strong></p> <p>We collect photos of commonly used chemical safety signs in chemical laboratories and chemistry teaching. For a discussion of the standards we base our collections, refer to the book "Talking about Hazardous Chemicals and Safety Signs" for common signs, and refer to the safety signs guidelines (GB 2894-2008).</p> <ul> <li>The shooting was mainly carried out in 6 locations, namely on the road, in a parking lot, construction walls, in a chemical laboratory, outside near big machines, and inside the factory and corridor.</li> <li>Shooting scale: Images in which the signs appear in small, medium and large scales were taken for each location by shooting photos from different distances.</li> <li>Shooting light: good lighting conditions and poor lighting conditions were investigated.</li> <li>Part of the images contain multiple targets and the other part contains only single signs.</li> </ul> <p>Under all conditions, a total of 4650 photos were taken in the original data. These were expanded to 27,900 photos were via data enhancement. All images are located in folder <code>ccss/data/JPEGImages</code>.</p> <p>The file <code>ccss/data/features/enhanced_data_to_original_data.csv</code> provides a mapping between the enhanced image name and the corresponding original image.</p> <p><strong>1.2. Annotation and Labelimg</strong></p> <p>We use Labelimg as labeling tool, which, in turn, uses the PASCAL-VOC labelimg format. The annotation is stored in the folder <code>ccss/data/Annotations</code>.</p> <p>Faster R-CNN and SSD are two algorithms that use this format. When training YOLOv5, you can run <code>trans_voc2yolo.py</code> to convert the XML file in PASCAL-VOC format to a txt file.</p> <p>We provide further meta-information about the dataset in form of a CSV file <code>features.csv</code> which notes, for each image, which other features it has (lighting conditions, scale, multiplicity, etc.). We apply the COCO standard for deciding whether a target is small, medium, or large in size.</p> <p><strong>1.3. Dataset Features</strong></p> <p>As stated above, the images have been shot under different conditions. We provide all the feature information in folder <code>ccss/data/features</code>. For each feature, there is a separate list of file names in that folder. The file <code>ccss/data/features/features_on_original_data.csv</code> is a CSV file which notes all the features of each original image.</p> <p><strong>1.4. Dataset Division</strong></p> <p>The data set is fixedly divided into 7:3 training set and test set. You can find the corresponding image names in the files <code>ccss/data/training_data_file_names.txt</code> and <code>ccss/data/test_data_file_names.txt</code>.</p> <p><strong><em>2. Baseline Experiments</em></strong></p> <p>We provide baseline results with five models, namely Faster R-CNN (R), Faster R-CNN (M), SSD, YOLOv3-spp, and YOLOv5. All code and results is given in folder <code>ccss/experiment</code>.</p> <p><strong>2.2. Environment and Configuration:</strong></p> <ul> <li>Single Intel Core i7-8700 CPU</li> <li>NVIDIA GTX1060 GPU</li> <li>16 GB of RAM</li> <li>Python: 3.8.10</li> <li>pytorch: 1.9.0</li> <li>pycocotools: pycocotools-win</li> <li>Visual Studio 2017</li> <li>Windows 10</li> </ul> <p><strong>2.3. Applied Models</strong></p> <p>The source codes and results of the applied models is given in folder <code>ccss/experiment</code> with sub-folders corresponding to the model names.</p> <p><em>2.3.1. Faster R-CNN</em></p> <ul> <li>Faster R-CNN (R) has the backbone resnet50+fpn. <ul> <li>we downloaded the pre-training weights from <a href="https://download.pytorch.org/models/fasterrcnn_resnet50_fpn_coco-258fb6c6.pth">https://download.pytorch.org/models/fasterrcnn_resnet50_fpn_coco-258fb6c6.pth</a></li> <li>we modify the type information of the JSON file to match our application.</li> <li>run <code>train_res50_fpn.py</code></li> <li>finally, the weights trained by the training set.</li> </ul> The Faster R-CNN (R) source code used in our experiment is given in folder <code>ccss/experiment/sources/faster_rcnn (R)</code>. The weights of the fully-trained Faster R-CNN (R) model are stored in file <code>ccss/experiment/trained_models/faster_rcnn (R).pth</code>. The performance measurements of Faster R-CNN (R) are stored in folder <code>ccss/experiment/performance_indicators/faster_rcnn (R)</code>.</li> <li>Faster R-CNN (M) has the backbone mobilenetv2. <ul> <li>backbone: MobileNetV2.</li> <li>we modify the type information of the JSON file to match our application.</li> <li>run <code>train_mobilenetv2.py</code></li> <li>finally, the weights trained by the training set.</li> </ul> The Faster R-CNN (M) source code used in our experiment is given in folder <code>ccss/experiment/sources/faster_rcnn (M)</code>. The weights of the fully-trained Faster R-CNN (M) model are stored in file <code>ccss/experiment/trained_models/faster_rcnn (M).pth</code>. The performance measurements of Faster R-CNN (M) are stored in folder <code>ccss/experiment/performance_indicators/faster_rcnn (M)</code>.</li> </ul> <p><em>2.3.2. SSD</em></p> <ul> <li>backbone: resnet50</li> <li>we downloaded pre-training weights from <a href="https://download.pytorch.org/models/resnet50-19c8e357.pth">https://download.pytorch.org/models/resnet50-19c8e357.pth</a></li> <li>the same training method as Faster R-CNN is applied.</li> </ul> <p>The SSD source code used in our experiment is given in folder <code>ccss/experiment/sources/ssd</code>. The weights of the fully-trained SSD model are stored in file <code>ccss/experiment/trained_models/ssd.pth</code>. The performance measurements of SSD are stored in folder <code>ccss/experiment/performance_indicators/ssd</code>.</p> <p><em>2.3.3. YOLOv3-spp</em></p> <ul> <li>backbone: DarkNet53</li> <li>we modified the type information of the XML file to match our application</li> <li>run <code>trans_voc2yolo.py</code> to convert the XML file in VOC format to a txt file.</li> <li>the weights used are: yolov3-spp-ultralytics-608.pt.</li> </ul> <p>The YOLOv3-spp source code used in our experiment is given in folder <code>ccss/experiment/sources/yolov3-spp</code>. The weights of the fully-trained YOLOv3-spp model are stored in file <code>ccss/experiment/trained_models/yolov3-spp.pt</code>. The performance measurements of YOLOv3-spp are stored in folder <code>ccss/experiment/performance_indicators/yolov3-spp</code>.</p> <p><em>2.3.4. YOLOv5</em></p> <ul> <li>backbone: CSP_DarkNet</li> <li>we modified the type information of the XML file to match our application</li> <li>run <code>trans_voc2yolo.py</code> to convert the XML file in VOC format to a txt file.</li> <li>the weights used are: yolov5s.</li> </ul> <p>The YOLOv5 source code used in our experiment is given in folder <code>ccss/experiment/sources/yolov5</code>. The weights of the fully-trained YOLOv5 model are stored in file <code>ccss/experiment/trained_models/yolov5.pt</code>. The performance measurements of YOLOv5 are stored in folder <code>ccss/experiment/performance_indicators/yolov5</code>.</p> <p><strong>2.4. Evaluation</strong></p> <p>The computed evaluation metrics as well as the code needed to compute them from our dataset are provided in the folder <code>ccss/experiment/performance_indicators</code>. They are provided over the complete test st as well as separately for the image features (over the test set).</p> <p><strong><em>3. Code Sources</em></strong></p> <ol> <li>Faster R-CNN (R and M) <ul> <li><a href="https://github.com/WZMIAOMIAO/deep-learning-for-image-processing/tree/master/pytorch_object_detection/faster_rcnn">https://github.com/WZMIAOMIAO/deep-learning-for-image-processing/tree/master/pytorch_object_detection/faster_rcnn</a></li> <li>official code: <a href="https://github.com/pytorch/vision/blob/main/torchvision/models/detection/faster_rcnn.py">https://github.com/pytorch/vision/blob/main/torchvision/models/detection/faster_rcnn.py</a></li> </ul> </li> <li>SSD <ul> <li><a href="https://github.com/WZMIAOMIAO/deep-learning-for-image-processing/tree/master/pytorch_object_detection/ssd">https://github.com/WZMIAOMIAO/deep-learning-for-image-processing/tree/master/pytorch_object_detection/ssd</a></li> <li>official code: <a href="https://github.com/pytorch/vision/blob/main/torchvision/models/detection/ssd.py">https://github.com/pytorch/vision/blob/main/torchvision/models/detection/ssd.py</a></li> </ul> </li> <li>YOLOv3-spp <ul> <li><a href="https://github.com/WZMIAOMIAO/deep-learning-for-image-processing/tree/master/pytorch_object_detection/yolov3-spp">https://github.com/WZMIAOMIAO/deep-learning-for-image-processing/tree/master/pytorch_object_detection/yolov3-spp</a></li> </ul> </li> <li>YOLOv5 <ul> <li><a href="https://github.com/ultralytics/yolov5">https://github.com/ultralytics/yolov5</a></li> </ul> </li> </ol> <p>We are particularly thankful to the author of the GitHub repository WZMIAOMIAO/deep-learning-for-image-processing (with whom we are not affiliated). Their instructive videos and codes were most helpful during our work. In particular, we based our own experimental codes on his work (and obtained permission to include it in this archive).</p>
Synthesis, Herbicidal Activity, Crop Safety and Soil Degradation of Pyrimidine- and Triazine-Substituted Chlorsulfuron Derivatives Supplementary Information
<p>The content include SI1-Melting points, 1H-NMR, 13C-NMR and HRMS of W103-W111, SI2-Crystal data of compound W110(CCDC number 2142702), SI3-Biological assay and crop safety, SI4-Soil degradation assay, SI5-Report of soil analysis in Chinese, and SI6-Report of soil analysis in English.</p>
Exploring traffic safety problems and challenges of older roads' users in Louisiana: Causes and countermeasures
<p>It is well established that older pedestrians and drivers with 65 years and above are among the most vulnerable road users. As the number and proportion of older road users (as drivers and pedestrians) grows in many countries, as well as their share in pedestrians’ and drivers’ crashes and injuries, it behooves transportation researchers to further investigate the safety and mobility challenges of older road users. This study aims mainly to provide a comprehensive investigation of older pedestrians’ and drivers’ safety challenges. To this end, a three-fold research approach is designed to thoroughly examine older road users’ safety challenges as pedestrians and drivers. First, crash data analysis identified significant risk factors causing/leading older drivers’ to be involved in vehicle crashes. Second, a driving simulator experiment was performed to further investigate the identified risky conditions from the crash data analysis and literature review. Third, a self-reported survey was conducted across the country to address pedestrians’ safety challenges, needs, and attitudes toward different pedestrian crossing facilities (i.e., signalized intersections, unsignalized intersections, midblock cross walks with and without flashing lights, and roundabouts). The results of this study provide a better understanding regarding older drivers’ and pedestrian’ needs and challenges that should be accommodated to improve their safety and mobility.</p>
Urban Transportation Infrastructure and Cyclist and Pedestrian Safety
<p>The goal of this project was to perform a comprehensive evaluation of crash causes and risk factors to identify the root causes of crashes involving bicyclists and pedestrians in San Antonio, TX. The research included the development of a database of bicycle and pedestrian crash reports in the target area, calculation of crash counts and rates, identifying road segments and intersections with highly concentrated bicycle and pedestrian crashes, and the development of effective safety countermeasures. Several variables and factors were analyzed, including driver characteristics such as age and gender, road-related factors, and environmental factors such as weather conditions and time of the day. Bivariate analysis and logistic regression were used to identify the most significant predictors of severe pedestrian/bicyclist crashes. Geospatial analysis was used to investigate crash frequency and severity. High-risk locations were identified through heat maps and hotspot analysis. The downtown area had the highest crash density, but crash severity hotspots were identified outside of the downtown area. The strongest predictors of severe injury include lighting condition, road class, road speed limit, traffic control, collision type, and the age and gender of the pedestrian/bicyclist. Fatal and incapacitating injury risk increased substantially when the pedestrian/bicyclist was at fault. Resource allocation to high-risk locations, a reduction in the speed limit, an upgrade of the lighting facilities in high pedestrian activity areas, educational campaigns for targeted audiences, the implementation of more crosswalks, pedestrian refuge islands, and raised medians, and the use of leading pedestrian/bicyclist interval and hybrid beacons are recommended.</p>
Safety of Vulnerable Road Users (VRU's) in Light-Rail Transit (LRT) Environment
<p>Light-rail transit (LRT), which includes modern streetcars, trolleys, and heritage trolleys, is one of the fastest growing modes of public transportation in the United States. To reduce the cost and complexity of construction, most LRT systems have their tracks placed on city streets, in medians, or in separate at-grade rights-of-way with at-grade crossings. Operating light-rail vehicles (LRVs) along these alignments introduces new conflicts and increases the risk of collisions with vulnerable road users (VRUs) including pedestrians, bicyclists, and electric scooter riders.<br> This study has two main objectives: (1) to review and evaluate the existing body of knowledge and the state of practice regarding safety of VRUs in LRT environments; and (2) to synthesize this information and package the results in a “Best Practices Resource Guide” and a companion “PowerPoint Presentation” for use in improving the safety of VRUs in existing LRT systems and advancing the professional capacity of transit workforce. Metropolitan Planning Organizations and State DOTs should also benefit from this resource information in the planning and design of new LRT systems.<br> This report presents a wide range of physical, educational, and enforcement treatments for improving the safety of VRUs in LRT environments. The selection of a particular treatment for use at an LRT grade crossing or station should be based on an engineering study whose scope and complexity depend on local conditions. Factors that should be considered during device selection include 1) pedestrian‐LRV collision experience, 2) pedestrian volumes and peak flow rates, 3) train speeds, frequency of trains, number of tracks, and railroad traffic patterns, 4) sight distances available to pedestrians and LRV operators approaching the crossing, and 5) skew angle, if any, of the crossing relative to the LRT tracks.</p>
Data from: Implementation of a pediatric telemedicine and medication delivery service in a resource-limited setting: A pilot study for clinical safety and feasibility
<p>Objective: Determine the clinical safety and feasibility of implementing a telemedicine and medication delivery service (TMDS) to address gaps in nighttime healthcare access for children in low-resource settings.</p> <p>Results: A total of 391 cases were enrolled from September 9th, 2019 to January 19th, 2021; 89% (347) received a household visit. Most cases were triaged as mild or moderate (92%; 361). Among the severe cases, 83% (20) sought subsequent referred care. The most common complaint was a respiratory problem (63%; 246). At 10-days, 95% (329) of parents reported their child's condition as "improved" or "recovered". Ninety-nine percent (344) rated the TMDS as "good" or "great". The median phone consultation was 20 minutes, time to arrival at the household was 73 minutes and total workflow per case was 114 minutes.</p> <p>Conclusion: The TMDS was a feasible healthcare delivery model with high rates of improved clinical status at 10-days.</p>
Factors related to the implementation of patient safety culture in prehospital emergency services
<p><span>Background</span><span>: Prehospital emergency services are one of the main areas of health care, which provide emergency services for patients who experience acute, critical illness or injury outside the hospital. Prehospital services are more often carried out by the Emergency Medical Services (EMS) team, as one of the health service providers who come to the scene more quickly, both in daily emergencies.</span></p> <p><span>Purpose</span><span>: </span><span>This study aims to determine the factors related to the application of patient safety culture and determine the most dominant factors in the application of patient safety culture at </span><span>the prehospital emergency service in Bantaeng.</span></p> <p><span>Results:</span><span> The results showed that all domains of patient safety culture had a significant effect (p<0.05) namely teamwork 0.000, safety climate 0.000, management perception and support 0.000, job satisfaction 0.019, work environment 0.000 and stress recognition 0.000. The most influential domain is stress recognition 1.116 and Adjust R Square value of 69.7%.</span></p> <p><span>Conclusion:</span><span> Patient safety needs to be a priority in handling and organizations must continue to evaluate the application of patient safety culture in PSC 119 setting in Bantaeng. Nurses are responsible for improving services that prioritize patient safety by understanding all factors related to the implementation of patient safety culture.</span></p>
Gastric Residual Volume, Safety and Effectiveness of Drinking 250ml Glucose Solution 2-3 Hours before Surgery in Gastric Cancer Patients: A Multicenter, Single-blind, Randomized Controlled Trial
<p><strong>Supplementary Figure 1</strong></p> <p> </p> <p>A. Change of QUICKI level after surgery</p> <p>Post-OP: right after operation; POD: post-operation day; Mixed linear model statistics showed that the effect of time ( p=0.004) was significant, but no significant differences were noted for group(P=0.600) or group*time(P=0.963).</p> <p>B. Change of Recovery Level of QUICKI after surgery</p> <p>Post-OP: right after operation; POD: post-operation day; Mixed linear model statistics showed that no significant differences were noted for group(P=0.600) ,time (P=0.235) or group×time(P=0.963).</p> <p>C. Change of WBC level after surgery</p> <p>Post-OP: right after operation; POD: post-operation day; Mixed linear model statistics showed that the effect of time ( p<0.001) was significant, and no significant differences were noted for group(P=0.392) or group×time(P=0.314).</p> <p>D. Change of CRP level after surgery</p> <p>Post-OP: right after operation; POD: post-operation day; Mixed linear model statistics showed that the effect of time ( p<0.001) was significant, but no significant differences were noted for group(P=0.312) or group × time(P=0.826).</p>
Safety of ChAdOx1 nCoV-19 vaccination in patients with end-stage renal disease on hemodialysis
<p>This dataset was the minimal dataset of the research article entitle 'Safety of ChAdOx1 nCoV-19 vaccination in patients with end-stage renal disease on hemodialysis'. The study aims to investigate the localized and systemic adverse effects (AEs) within 30-days after the first dose of ChAdOx1 nCoV19 (Oxford-AstraZeneca) vaccination in end-stage renal disease (ESRD) patients receiving hemodialysis (HD). The first dataset is the raw data for logistic regression analyses examining the risk factors in association with AEs, another dataset contains the second- and the seventh- day incidences of various AEs depicted in bar graphs. It comprises variables recording the status of patient-reported local and systemic AEs in questionnaire on the second and seventh days after vaccination as well as patients' baseline demographic and clinical characteristics.</p>
Worrying confessions: A look at data safety labels on Android
<p>The Google Play Store recently introduced a data safety section in order to give users accessible insights into apps’ data collection practices. We analyzed the labels of 43,927 of the most popular apps. Almost one third of the apps with a label claims not to collect any data. But we also saw popular apps, including apps meant for children, admitting to collecting and sharing highly sensitive data like the user’s sexual orientation or health information for tracking and advertising purposes. To verify the declarations, we recorded the network traffic of 500 apps, finding more than one quarter of them transmitting tracking data not declared in their data safety label.</p> <p>This data set contains a dump of our database, including the top chart data and data safety labels from September 07, 2022, and the recorded network traffic.</p> <p>The analysis is available at our blog: <a href="https://www.datarequests.org/blog/android-data-safety-labels-analysis/">https://www.datarequests.org/blog/android-data-safety-labels-analysis/</a><br> The source code for the analysis is available on GitHub: <a href="https://github.com/datenanfragen/android-data-safety-label-analysis">https://github.com/datenanfragen/android-data-safety-label-analysis</a></p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.