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Association Between Multimorbidity and Postoperative Mortality in Patients Undergoing Major Surgery: A Prospective Study in 29 Countries Across Europe
Get PDFBackground Multimorbidity poses a global challenge to healthcare delivery. This study aimed to describe the prevalence of multimorbidity, common disease combinations and outcomes in a contemporary cohort of patients undergoing major abdominal surgery. Methods This was a pre-planned analysis of a prospective, multicentre, international study investigating cardiovascular complications after major abdominal surgery conducted in 446 hospitals in 29 countries across Europe. The primary outcome was 30-day postoperative mortality. The secondary outcome measure was the incidence of complications within 30 days of surgery. Results Of 24,227 patients, 7006 (28.9%) had one long-term condition and 10,486 (43.9%) had multimorbidity (two or more long-term health conditions). The most common conditions were primary cancer (39.6%); hypertension (37.9%); chronic kidney disease (17.4%); and diabetes (15.4%). Patients with multimorbidity had a higher incidence of frailty compared with patients ≤ 1 long-term health condition. Mortality was higher in patients with one long-term health condition (adjusted odds ratio 1.93 (95%CI 1.16–3.23)) and multimorbidity (adjusted odds ratio 2.22 (95%CI 1.35–3.64)). Frailty and ASA physical status 3–5 mediated an estimated 31.7% of the 30-day mortality in patients with one long-term health condition (adjusted odds ratio 1.30 (95%CI 1.12–1.51)) and an estimated 36.9% of the 30-day mortality in patients with multimorbidity (adjusted odds ratio 1.61 (95%CI 1.36–1.91)). There was no improvement in 30-day mortality in patients with multimorbidity who received pre-operative medical assessment. Conclusions Multimorbidity is common and outcomes are poor among surgical patients across Europe. Addressing multimorbidity in elective and emergency patients requires innovative strategies to account for frailty and disease control. The development of such strategies, that integrate care targeting whole surgical pathways to strengthen current systems, is urgently needed for multimorbid patients. Interventional trials are warranted to determine the effectiveness of targeted management for surgical patients with multimorbidity
It Is Not All Black and White: The Effect of Increasing Severity of Frailty on Outcomes of Geriatric Trauma Patients
No full textBackground: Frailty is associated with poor outcomes in trauma patients. However, the spectrum of physiologic deficits, once a patient is identified as frail, is unknown. The aim of this study was to assess the dynamic association between increasing frailty and outcomes among frail geriatric trauma patients. Methods: This is a secondary analysis of the American Association of Surgery for Trauma Frailty Multi-institutional Trial. Patients 65 years or older presenting to one of the 17 trauma centers over 3 years (2019-2022) were included. Frailty was assessed within 24 hours of presentation using the Trauma-Specific Frailty Index (TSFI) questionnaire. Patients were stratified by TSFI score into six groups: nonfrail (\u3c0.12), Grade I (0.12-0.19), Grade II (0.20-0.29), Grade III (0.30-0.39), Grade IV (0.40-0.49), and Grade V (0.50-1). Our Outcomes included in-hospital and 3-month postdischarge mortality, major complications, readmissions, and fall recurrence. Multivariable regression analyses were performed. Results: There were 1,321 patients identified. The mean (SD) age was 77 years (8.6 years) and 49% were males. Median [interquartile range] Injury Severity Score was 9 [5-13] and 69% presented after a low-level fall. Overall, 14% developed major complications and 5% died during the index admission. Among survivors, 1,116 patients had a complete follow-up, 16% were readmitted within 3 months, 6% had a fall recurrence, 7% had a complication, and 2% died within 3 months postdischarge. On multivariable regression, every 0.1 increase in the TSFI score was independently associated with higher odds of index-admission mortality and major complications, and 3 months postdischarge mortality, readmissions, major complications, and fall recurrence. Conclusion: The frailty syndrome goes beyond a binary stratification of patients into nonfrail and frail and should be considered as a spectrum of increasing vulnerability to poor outcomes. Frailty scoring can be used in developing guidelines, patient management, prognostication, and care discussions with patients and their families
The Native American Graves Protection and Repatriation Act (NAGPRA)
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Investigation of Spalart-Allmaras Turbulence Model for Vortex Flows
Get PDFConventional turbulence models often predict behaviors opposite as to what is observed in flows subject to rotation. In this type of flow scenario, rotation typically induces turbulence suppression. To address this limitation, a modification to the Spalart Allmaras Model with Rotation Correction (SA-R) was proposed to enhance the original Spalart Allmaras Model’s sensitivity to rotation and curvature. To test the validity and accuracy of this modification, two cases were investigated. The first case involved an axisymmetric rotating pipe. A Reynolds Number of 37,000 was implemented and the initial and boundary conditions established by Zaets et. al. were utilized. Initially non-rotating, the flow transitioned to full rotation at N=0.6 at 9 m. Results demonstrated strong alignment with experimental data, showcasing improvements over the SA , SA-R, SARC, and SA-R23 models. In the second case, a vortex, surrounded by irrotational flow, was studied. This case used a Reynolds number of 10^5, and implemented the initial and boundary conditions outlined by Spalart and Garbaruk. While the modified model showed improvement over the SA model, it still displayed slight circulation overshoot, a behavior considered unphysical. However, it notably reduced the magnitude of eddy viscosity. The SARC model did produce a laminar state solution. Other vortex parameters also indicated circulation overshoot of the modified SA-R model. Overall, the modified SA-R model showed significant improvement for rotational flow scenarios and holds potential for further refinement to improve accuracy
Peer-Assisted Learning in Miller Analogies Tasks
Get PDFThe present study aimed to investigate the role of peer-assisted learning (PAL) on individual performance using a relatively complex Miller Analogies Task (MAT). I found that low-ability learners benefitted from PAL. Furthermore, this may be explained by a significant trust mechanism indicating that learners who correctly identified trustworthy peers had greater performance. I did not find support for a significant difference between PAL and individual learning conditions. This study provides evidence for the role of scaffolding, where low-ability learners may benefit from identifying who to guide them when engaging in a social peer-learning task
Underuse of Pragmatic Markers among Non-native English Speakers: Causes and Suggestions for Interventions
Get PDFPragmatic markers (PMs)—those optional markers used in conversations to facilitate communication—have been gaining attention among researchers in the field of pragmatics in the past three decades or so. Proper use of PMs contributes to the success of any interaction in real-life conversations. However, they are often underused by non-native English speakers (NNSs). This paper critically reviews the existing research literature to investigate the causes of the underuse of PMs among NNSs, focusing on linguistic and extralinguistic factors that might contribute to the limited use of PMs. The findings of this study show that the distinctive nature of pragmatic development in the first and second languages, lack of instructions on PMs, limited exposure to the target language, and the NNSs\u27 level of proficiency are the main linguistic issues that arise during pragmatic development. Gender and age, as non-linguistic factors, also limit the use of PMs. These findings suggest that linguistic and extralinguistic factors constrain the use of PMs in NNSs’ speech. Based on the findings, suggestions for interventions are offered for language educators to better integrate PM use in ESL/EFL teaching
Test-time Backdoor Attack Using Universal Perturbation
Get PDFThe rapid growth and widespread reliance on machine learning (ML) systems across critical applications such as healthcare, autonomous driving, and cybersecurity have un- derscored their transformative potential and heightened their susceptibility to adversarial attacks and vulnerabilities. This thesis investigates vulnerabilities in ML models, focusing on backdoor attacks, including naive backdoor attack, feature collision backdoor attack, hidden trigger backdoor attack, and test-time backdoor attack using universal perturbation technique. These methodologies demonstrate how adversaries can automate and conceal malicious behaviors to achieve specific objectives, posing significant challenges to ML model integrity and trustworthiness. The research provides a comprehensive analysis of the theoretical foundations and prac- tical implications of these attack vectors. The naive backdoor attack establishes a baseline, highlighting its simplicity and effectiveness despite limited stealth. Feature collision back- door attack leverages neural networks to exploit overlaps in the feature space, addressing mislabeled issue yet yielding lower attack success rates. Hidden trigger attack emphasizes stealth by embedding imperceptible patterns into training datasets, enabling targeted ma- nipulations with high discretion. Test-time backdoor attack using universal perturbation introduces a novel approach that applies imperceptible perturbations during inference, by- passing the need for poisoned training data or model retraining. This method addresses practical constraints, allowing adversaries to execute covert attacks even without access to the target model or dataset. By exploring these advanced backdoor attack techniques, this thesis contributes to un- derstanding the vulnerabilities inherent in ML systems and highlights critical challenges in defending against stealthy and automated adversarial behaviors
Meta-Learning-Based Model Stacking Framework for Hardware Trojan Detection in FPGA Systems
Get PDFIn today\u27s technological landscape, hardware devices are integral to critical applications such as industrial automation, autonomous vehicles, and medical equipment, relying on advanced platforms like FPGAs for core functionalities. However, the multi-stage manufacturing process, often distributed across various foundries, introduces substantial security risks, notably the potential for hardware Trojan insertion. These malicious modifications compromise the reliability and safety of hardware systems. This research addresses the detection of hardware Trojans through side-channel analysis, utilizing power and electromagnetic signal data, combined with meta-learning techniques, specifically model stacking. By employing diverse base models and a meta-model to consolidate predictions, this non-invasive approach effectively identifies Trojans without requiring direct access to internal circuitry. The methodology demonstrates robust classification capabilities, achieving an accuracy of 88.0%, precision of 81.0%, and recall of 95.0%, even on previously unseen data. The results highlight the superior performance of meta-learning over traditional detection methods, offering an efficient and reliable solution to enhance hardware security
The Extension Newsletter, Issue 114, Spring 2024
Get PDFAn eight page newsletter from the Wright State University\u27s Retirees Association.https://corescholar.libraries.wright.edu/wsura_newsletter/1112/thumbnail.jp