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273 results for “alert”
Randomized trial of AKI alerts in hospitalized patients
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Automated, medication-targeted alerts for Acute Kidney Injury – A randomized trial
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Soil microbe-induced plant volatiles can alert neighboring plants for tolerating heavy metal stress
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Data from: Sepsis surveillance: an examination of parameter sensitivity and alert reliability
Objective: To examine performance of a sepsis surveillance system in a simulated environment where modifications to parameters and settings for identification of at-risk patients can be explored in-depth. Materials and Methods: This was a multiple center observational cohort study. The study population comprised 14 917 adults hospitalized in 2016. An expert-driven rules algorithm was applied against 15.1 million data points to simulate a system with binary notification of sepsis events. Three system scenarios were examined: a scenario as derived from the second version of the Consensus Definitions for Sepsis and Septic Shock (SEP-2), the same scenario but without systolic blood pressure (SBP) decrease criteria (near SEP-2), and a conservative scenario with limited parameters. Patients identified by scenarios as being at-risk for sepsis were assessed for suspected infection. Multivariate binary logistic regression models estimated mortality risk among patients with suspected infection. Results: First, the SEP-2-based scenario had a hyperactive, unreliable parameter SBP decrease >40 mm Hg from baseline. Second, the near SEP-2 scenario demonstrated adequate reliability and sensitivity. Third, the conservative scenario had modestly higher reliability, but sensitivity degraded quickly. Parameters differed in predicting mortality risk and represented a substitution effect between scenarios. Discussion: Configuration of parameters and alert criteria have implications for patient identification and predicted outcomes. Conclusion: Performance of scenarios was associated with scenario design. A single hyperactive, unreliable parameter may negatively influence adoption of the system. A trade-off between modest improvements in alert reliability corresponded to a steep decline in condition sensitivity in scenarios explored.
Data from: Medical-device recalls in the UK and the device-regulation process: retrospective review of safety notices and alerts
Background: Medical devices are used widely for virtually every disease and condition. Although devices are subject to regulation, the number of recalls, the clinical data requirements for regulation and the impact on patient safety are poorly understood. Methods: The authors defined a device using European directives and used publicly available information on the Medicines and Health Regulatory Authority website to determine the number of devices recalled from January 2006 to December 2010. Two reviewers independently assessed Field Safety Notices and Medical Device Alerts. The authors wrote to manufacturers to obtain further information and clinical data, and summarised data by year, Conformité Européenne classification, indication, and Food and Drug Administration recall system of severity. Results: In total, 2124 field safety notices were issued over the 5-year period, an increase of 1220% (62 in 2006 to 757 in 2010). 447 Medical Device Alerts were issued in the same period, and 44% were assessed as a reasonable probability of causing serious adverse health consequences or death. The authors wrote to 192 manufacturers of withdrawn devices and received 101 (53%) replies; only four (2.1%) provided the clinical data the authors requested. A lack of available transparent data prevented full analyses of the safety impact. Of the highest-risk recalled devices, more than half were related to the cardiovascular system (25%) or musculoskeletal system (33%), and 88% (95% CI 80% to 97%) were assessed as a reasonable probability of causing serious adverse health consequences or death. For low-risk devices, the figure was 34% (95% CI 26% to 42%). Conclusion: The number of medical devices subject to recalls or warnings in the UK has risen dramatically. A substantial number of these devices may have caused serious adverse effects in patients and contributed to healthcare costs. Significant problems exist in the UK with a lack of access to transparent data and a registry of the highest-risk devices.
Data from: Plant defence responses to volatile alert signals are population-specific
Herbivore-induced volatiles are widespread in plants. They can serve as alert signals that enable neighbouring leaves and plants to pre-emptively increase defences and avoid herbivory damage. However, our understanding of the factors mediating volatile organic compound (VOC) signal interpretation by receiver plants and the degree to which multiple herbivores affect VOC signals is still limited. Here we investigated whether plant responses to damage-induced VOC signals were population specific. As a secondary goal, we tested for interference in signal production or reception when plants were subjected to multiple types of herbivore damage. We factorially crossed the population sources of paired Phaseolus lunatus plants (same versus different population sources) with a mechanical damage treatment to one member of the pair (i.e. the VOC emitter, damaged versus control), and we measured herbivore damage to the other plant (the VOC receiver) in the field. Prior to the experiment, both emitter and receiver plants were naturally colonized by aphids, enabling us to test the hypothesis that damage from sap-feeding herbivores interferes with VOC communication by including emitter and receiver aphid abundances as covariates in our analyses. One week after mechanical leaf damage, we removed all the emitter plants from the field and conducted fortnightly surveys of leaf herbivory. We found evidence that receiver plants responded using population-specific 'dialects' where only receivers from the same source population as the damaged emitters suffered less leaf damage upon exposure to the volatile signals. We also found that the abundance of aphids on both emitter and receiver plants did not alter this volatile signalling during both production and reception despite well-documented defence crosstalk within individual plants that are simultaneously attacked by multiple herbivores. Overall, these results show that plant communication is highly sensitive to genetic relatedness between emitter and receiver plants and that communication is resilient to herbivore co-infestation.
Dataset: "Auralization of Electric Vehicles for the Perceptual Evaluation of Acoustic Vehicle Alerting Systems"
<p>This repository contains audio examples and measurement data accompanying the paper: </p> <blockquote> <p>Müller L. & Kropp W. 2024. Auralization of electric vehicles for the perceptual evaluation of acoustic vehicle alerting systems. Acta Acustica, 8, 27. https://doi.org/10.1051/aacus/2024025</p> </blockquote> <p>The Matlab code for the corresponding auralization model can be found at: <a href="https://github.com/leonpaulmueller/evat" target="_blank" rel="noopener">https://github.com/leonpaulmueller/evat</a></p> <p> </p> <p><strong>Content</strong></p> <ul> <li><code>audio_examples.zip</code> <ul> <li>avas - Measured and synthesized AVAS source signals</li> <li>passby - Measured and auralized binaural EV passages at roadside observer position. The generated signals use the same vehicle velocity as the corresponding measurements.</li> <li>tire - Measured and synthesized tire/road noise source signals</li> </ul> </li> <li><code>measurements.zip</code> <ul> <li>ambience - binaural ambience measurements</li> <li>avas - AVAS source signal measurements</li> <li>passby - Binaural pass-by measurements, including velocity data and isolated AVAS and tire/road noise signals</li> <li>tires - tire/road noise measurements</li> </ul> </li> </ul> <p> </p> <p>For consistency with the paper, we use the following aliases for the three evaluated vehicles:</p> <ul> <li>Vehicle A: Tesla Model Y 2021</li> <li>Vehicle B: Volkswagen ID.3 Pro Performance 2021</li> <li>Vehicle C: Nissan Leaf 2018</li> </ul>
Screening of 6000 compounds for uncoupling activity: input parameters, the predicted uncoupling activity, as well as the results in respect to structural alerts
<p>Protonophoric uncoupling of phosphorylation is an important factor when assessing chemicals for their toxicity, and has recently moved into focus in pharmaceutical research with respect to the treatment of diseases such as cancer, diabetes or obesity. Reliably identifying uncoupling activity is thus a valuable goal. To that end, we screened more than 6000 anionic compounds for in-vitro uncoupling activity, using a biophysical model based on ab-initio COSMO-RS input parameters with the molecular structure as the only external input. We combined these results with a model for baseline toxicity (narcosis). Our model identified more than 1250 possible uncouplers in the screening dataset, and identified possible new uncoupler classes such as thiophosphoric acids. When tested against 423 known uncouplers and 612 known inactive compounds in the dataset, the model reached a sensitivity of 83% and a specificity of 96%. In a direct comparison, it showed a similar specificity than the structural alert profiler Mitotox (97%), but much higher sensitivity than Mitotox (47%). The biophysical model thus allows for a more accurate screening for uncoupling activity than existing structural alert profilers. We propose to use our model as a complementary tool to screen large datasets for protonophoric uncoupling activity in drug development and toxicity assessment.</p>
Alert burden in pediatric hospitals: A cross-sectional analysis of six academic pediatric health systems using novel metrics
<p class="Pediatrics">Background: Excessive electronic health record (EHR) alerts reduce the salience of actionable alerts. Little is known about the frequency of interruptive alerts across health systems and how the choice of metric affects which users appear to have the highest alert burden.</p> <p class="Pediatrics">Objective: (1) Analyze alert burden by alert type, care setting, provider type, and individual provider across 6 pediatric health systems. (2) Compare alert burden using different metrics.</p> <p class="Pediatrics">Materials and Methods: We analyzed interruptive alert firings logged in EHR databases at 6 pediatric health systems from 2016-2019 using 4 metrics: (1) alerts per patient encounter, (2) alerts per patient-day, (3) alerts per 100 orders, and (4) alerts per unique clinician days (calendar days with at least one EHR log in the system). We assessed intra- and inter-institutional variation and how alert burden rankings differed based on the chosen metric.</p> <p class="Pediatrics">Results: Alert burden varied widely across institutions, ranging from 0.06 to 0.76 firings per encounter, 0.22 to 1.06 firings per inpatient-day, 0.98 to 17.42 per 100 orders, and 0.08 to 3.34 firings per clinician day logged in the EHR. Custom alerts accounted for the greatest burden at all 6 sites. The rank order of institutions by alert burden was similar regardless of which alert burden metric was chosen. Within institutions, the alert burden metric choice substantially affected which provider types and care settings appeared to experience the highest alert burden.</p> <p>Conclusion: Estimates of the clinical areas with highest alert burden varied substantially by institution and based on the metric used.</p>
Bank Alerts
<p>alerts.csv : alert data</p> <p>pattern_label.csv & pattern_label.xlsx: alert patterns from experts</p>
Alert Dataset
<p>Synthetic alert data with geolocation for displaying on map-based visualisations.</p>
Using Consumer-grade Wearable Devices for Fall Risk Evaluation and Alerts
ClinicalTrials.gov study NCT06508892. IPD Sharing: NO. Countries: 1. Publications: 2.
Diuretics vs. Afterload Reduction for Treatment of HeartLogic Alerts
ClinicalTrials.gov study NCT06218199. IPD Sharing: Not stated. Countries: 1. Publications: 1.
VR Breaks on Shift-worker Alertness
ClinicalTrials.gov study NCT04132141. IPD Sharing: NO. Countries: 1. Publications: 5.
Using Clinical Alerts to Decrease Inappropriate Medication Prescribing
ClinicalTrials.gov study NCT01034761. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Effects of a Platelet Transfusion Best Practices Alert
ClinicalTrials.gov study NCT04155775. IPD Sharing: NO. Countries: 1. Publications: 1.
Epidogs International Inventory of Seizure Alert Dogs
ClinicalTrials.gov study NCT03655366. IPD Sharing: NO. Countries: 1. Publications: 1.
The Effectiveness of a Chatbot-facilitated High Alert Medication Education for 2-year Post Graduate Nurses
ClinicalTrials.gov study NCT05985005. IPD Sharing: YES. Countries: 1. Publications: 0.
Machine Learning Sepsis Alert Notification Using Clinical Data
ClinicalTrials.gov study NCT04005001. IPD Sharing: NO. Countries: 1. Publications: 5.
Effects of Weight Reduction on Sleep and Alertness in Long-distance Truck and Bus Drivers
ClinicalTrials.gov study NCT00893646. IPD Sharing: Not stated. Countries: 1. Publications: 1.
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.