Original Decision Research Articles

Predictive Mental Workload Modeling Methodology for Single-Pilot Operations System Design

Predictive mental workload model is an important tool for evaluating early single-pilot operations (SPO) system design and can be used to explore the impact of different function allocation decisions before prototype or simulator implementation. To build this model, this paper proposes a methodology based on task analysis. Firstly, it selects the approach and landing scenario of the current two-pilot operations (TCO) and completes the task description of the TCO using the hierarchical task analysis. Secondly, it identifies the function allocation requirements for the transition from TCO to SPO using the work domain analysis and proposes the original function allocation decisions based on the SPO’s system framework. Finally, it creates the predictive mental workload model for evaluating different SPO system design options based on temporal network and multiple resource theory. The researchers use the created model to evaluate the original function allocation decisions and find that the designed SPO system has instantaneous workload values that exceed the upper limit at multiple time points. By analyzing the causes of these problems, the researchers introduce audible warnings and visual indicators to the SPO system to improve design decisions and evaluate these decisions. The results show that the continuous workload profile and overall average workload of the redesigned SPO system are better than the current TCO system, and also prove that the predictive mental workload model can be well applied to evaluate the system design of early SPO.

Adversarial Deep Learning based Dampster–Shafer data fusion model for intelligent transportation system

Intelligent Transportation Systems (ITS) have revolutionized transportation by incorporating advanced technologies for efficient and safe mobility. However, these systems face challenges ensuring security and resilience against adversarial attacks. This research addresses these challenges and introduces a novel Dampster–Shafer data fusion-based Adversarial Deep Learning (DS-ADL) Model for ITS in fog cloud environments. Our proposed model focuses on three levels of adversarial attacks: original image level, feature level, and decision level. Adversarial examples are generated at each level to evaluate the system’s vulnerability comprehensively. To enhance the system’s capabilities, we leverage the power of several vital components. Firstly, we employ Dempster–Shafer-based Multimodal Sensor Fusion, enabling the fusion of information from multiple sensors for improved scene understanding. This fusion approach enhances the system’s perception and decision-making abilities. For feature extraction and classification, we utilize ResNet 101, a deep learning architecture known for its effectiveness in computer vision tasks. We introduced a novel Monomodal Multidimensional Gaussian Model (MMGM-DD) based Adversarial Detection approach to detect adversarial examples. This detection mechanism enhances the system’s ability to identify and mitigate adversarial attacks in real-time. Additionally, we incorporate the Defensive Distillation method for adversarial training, which trains the model to be robust against attacks by exposing it to adversarial examples during the training process. To evaluate the performance of our proposed model, we utilize two datasets: Google Speech Command version 0.01 and the German Traffic Sign Recognition Benchmark (GTSRB). Evaluation metrics include latency delay and computation time (fog–cloud), accuracy, MSE, loss, and F-score for attack detection and defense. The results and discussions demonstrate the effectiveness of our Dampster–Shafer data fusion-based Adversarial Deep Learning Model in enhancing the robustness and security of ITS in fog–cloud environments. The model’s ability to detect and defend against adversarial attacks while maintaining low-latency fog–cloud operations highlights its potential for real-world deployment in ITS.

The impact of country of origin on consumer purchase decision of luxury vs. fast fashion: case of Saudi female consumers

The purpose of this paper is to examine the impact of country of origin on purchase decisions of luxury and fast fashion. The countries of origin include the US and European countries like France, Italy, UK, Spain, Germany, and Sweden. Moreover, conspicuous consumption was included in this research as a mediating variable, and brands from the US and Europe were evaluated. A total of 443 women were surveyed. The data were assessed using a structural equation model and a t-test. The results confirm the positive relationship between the country of origin and purchase decision of luxury and fast fashion; conspicuous consumption acted as a mediating variable that affected this relation. The results also confirm the positive relationship between conspicuous consumption and consumer purchase decisions of luxury and fast fashion brands. No difference was found between luxury and fast fashion in terms of the relation to the country of origin and conspicuous consumption. Moreover, US brands were evaluated higher than European ones for both luxury and fast fashion. This study gives insight into the role of country of origin in purchase decisions. In addition, it provides a deep understanding of the term of conspicuous consumption and its relation not only to luxury but also to fast fashion.

Clinical Outcomes Based on Coronary Computed Tomography-Derived Fractional Flow Reserve and Plaque Characterization

BackgroundCoronary computed tomography angiography (CTA) followed by computed tomography angiography–derived fractional flow reserve (FFRCT) is now commonly used for the management of chronic coronary syndrome (CCS). CTA-verified high-risk plaque (HRP) characteristics have also been reported to be associated with a greater likelihood of adverse cardiac events but have not been used for management decisions. ObjectivesThe aim of this study was to evaluate clinical outcomes based on a combination of point-of-care computed tomography angiography–derived fractional flow reserve (POC-FFRCT) and the presence of HRP in CCS patients initially treated medically or with revascularization based on invasive coronary angiography findings. MethodsCTA was performed as the initial test in 5,483 patients presenting with CCS between September 2015 and December 2020 followed by invasive coronary angiography and revascularization as necessary. POC-FFRCT assessment and HRP characterization were obtained subsequently in 745 consecutive patients. We investigated how HRP and POC-FFRCT, which were not available during the original clinical decision making, correlated with the endpoint defined as a composite of cardiac death, acute coronary syndrome, and a need for unplanned revascularization. ResultsCardiac events occurred in 20 patients (2.7%) during a median follow-up of 744 days. The event rate was significantly higher in patients with POC-FFRCT <0.80 compared with POC-FFRCT ≥0.8 (5.4 vs 0.5 per 100 vessel years; log-rank P < 0.0001) and in patients with HRP compared to those without HRP (3.6 vs 0.8 per 100 vessel years; log-rank P = 0.0001). POC-FFRCT <0.80 and the presence of HRP were the independent predictors of cardiac events (HR: 16.67; 95% CI: 2.63-105.39; P = 0.002) compared with POC-FFRCT ≥0.8 and absent HRP. For the vessels with POC-FFRCT <0.80 and HRP, a significantly higher rate of adverse events was observed in patients who did not undergo revascularization compared with those revascularized (16.4 vs 1.4 per 100 vessel years; log-rank P = 0.006). ConclusionsPOC-FFRCT <0.80 and the presence of HRP were the independent predictors of cardiac events, and revascularization of HRP lesions with abnormal POC-FFRCT was associated with a lower event rate.

Fuzzy Decision Rule-Based Online Classification Algorithm in Fuzzy Formal Decision Contexts

Formal concept analysis (FCA) extracts interpretable rules by using implication relationships between concepts, which is an effective method for acquiring knowledge. In this paper, a fuzzy decision rule-based online classification algorithm called OFRCA is designed by combining online learning theory and FCA in a fuzzy formal decision context. First, the original weight vector of all attributes is obtained by fusing the original deterministic decision rules. Second, incremental fuzzy decision rules are obtained according to the added objects. Then for the incremental fuzzy decision rules, OFRCA updates the weight vector based on minimizing the loss function. At the end of the process, all the rules are fused into an attributeweighted classifier with the final weight vector. In this paper, we employ the regret to obtain a learning guarantee for OFRCA. The growth rate of OFRCA's regret converges to 0 as the number of iterations increases in the ideal state, which provides an effective learning guarantee for OFRCA. To verify the effectiveness of the proposed algorithm, this paper conducted numerical experiments and systematically analyzed the experimental results, which demonstrated that OFRCA achieved the highest advanced classification performance among the compared algorithms.

Tertiary Education, Changing One’s Educational Decision and the Role of Parental Preferences

Unequal access to university and the decision processes that give rise to it are important factors in the accumulation of educational inequalities. In this paper, we investigate a specific aspect of such decision processes by focusing on those students who change their original plans to start a (nontertiary) vocational education and decide to pursue a tertiary degree instead. In doing so, we find that more than one-fifth of the students in our sample who originally planned to pursue a vocational education change their original decision in this way. Moreover, while students from a more advantaged background are more likely to go to university in the first place, those among them that initially opted for a vocational education are also more likely to change their decision and go to university instead. We also find that parental preferences for tertiary education play an important role in the process of changing one’s mind, even for adult children. Moreover, we find that differential parental preferences contribute to both the emergence of social background effects and—as a result—the perpetuation of educational inequalities.

A new interval meta-goal programming for sustainable planning of agricultural water-land use nexus

Meta-Goal Programming (MGP) is a simultaneous cognitive evaluation of the degree of achievements for original decision goals considered in a GP model. However, in most real-world situations, environmental coefficients and related parameters are not easily available. In such a situation, the decision-maker must consider various conflicting targets in a framework of uncertain aspiration levels at the same time. On the other side, Interval Programming (IP) is a method used to increase the range of available decision-maker preference structures in GP. In the perspective of solving the conflicts between agriculture and water use towards sustainability, this paper proposes an Interval Meta-Goal Programming Model (IMGPM) dealing with imprecision in data that covers interval coefficients, target intervals, and interval bounds of meta-goals. This novel methodology has been tested in a study area in Iran to validate its added value in solving conflicting uses of natural resources by economic sectors. This integration together with its application for sustainable optimal cropping patterns (agroecosystem planning) represents a novelty in the field of ecological modeling. The management solutions of our method in terms of land allocation are different from those in Sen and Pal (2013) model. In the case of Iran, many socio-ecological-economic strategies and policies should be necessary for improving the agricultural sector. More specifically, on the basis of rainfall amounts and spatial patterns, this approach can represent a decision-support system able to define strategies for additional water storage useful to support crop production. Furthermore, the availability of water together with the sustainable use of fertilizers can mitigate the risk of land degradation, guaranteeing people employment, food security, and economic profits. Although the present methodology seems to solve the problem of multi-goals decision-making, the inclusion of spatial relationships is able to introduce dependencies between the management of land use in adjacent areas, making the present approach nearer to real-world functioning.

Securitisation in the Mediterranean: an ethical analysis of the EUNAVFOR MED SOPHIA operation using the prism of Floyd’s Just Securitisation Theory (JST)

ABSTRACT In May 2015, the EU decided to launch a common military response against immigrant traffickers and smugglers known as the European Union Naval Force Mediterranean (EUNAVFOR MED)—and Operation SOPHIA. Its aim was to dispose of smugglers' vessels and other smuggling assets while still in Libyan territorial waters. Although this specific aim had never materialized, leading to the operation's termination in March 2020, the original decision had led to a securitisation process in the Mediterranean in the form of an increasing deployment of militarized bordering practices and techniques under the umbrella of Operation EUVAVFOR MED. The present paper presents a normative analysis of EUNAVFOR MED through the prism of Floyd's Just Securitisation Theory (JST). It focuses on the just initiation of securitisation, which is the first element of JST and a parallel to JWT's ius ad bellum that contains five conditions: Just Reason for Securitisation; Morally Justifiable Referent Objects; Right Intention; Macro Proportionality, and Reasonable Chances of Success. The paper concludes these conditions were not all met in all three phases of EUVAVFORMED and highlights the complexity of normatively assessing the securitisation of migrants' smugglers in the context of this European maritime military operation.

Reliance arguments, democratic law, and inequity

ABSTRACT The reversal of Roe v. Wade raises the prospect that other due process guarantees upon which individuals have organised their lives, including the constitutional rights to same-sex intimacy and marriage, will be overturned. These potential upheavals in the hard-won legal infrastructure of basic social status call for a careful look at reliance arguments for sustaining constitutional precedent. When does reliance on a judicial decision provide reason for a court to sustain a precedent in the face of substantial doubts or convictions that the original decision was importantly mistaken? Our commitments to democratic law pose distinctive challenges for reliance arguments in the public law domain that do not burden reliance arguments in the private law domain. A persuasive reliance argument in the public law context must offer a merits-independent rationale identifying limited circumstances in which reliance would justify honouring a prior (mistaken) decision and honouring it prospectively, into the future. I detail the challenges and offer a democratic account to identify when overturning a mistaken decision would impose an unfair and inequitable hardship. On this account, abortion, contraception, and same-sex intimacy rights warrant continued affirmation given reliance behaviours, but not other rights, including rights to gun ownership and freedom of contract.

Pinguicula lithophytica is not conspecific with Pinguicula jaraguana or a variety of Pinguicula jackii

The taxonomic status of Pinguicula lithophytica had been challenged both in publications and in discussions. It was proposed that P. lithophytica is conspecific with Pinguicula jaraguana. Both taxa represent Cuban endemics but they are from widely separate locations. Comparison with P. jaraguana was not considered by Panfet Valdés and Temple as the taxon was unknown to the authors; subsequent awareness did not cause the authors to question their original decision that assigned species status to P. lithophytica that some individuals later perceived demonstrated no significant differences to P. jaraguana. Recently published and even more recent unpublished information concerning P. jaraguana has allowed comparison with previously unpublished information about P. lithophytica. Comparison of the morphology of both species, especially of newly described seeds and new or enhanced measurement of leaf length and width and flower characteristics reveals sufficient differences to attest to their being separate taxa. The name Pinguicula lithophytica was also presented as synonymous with P. jackii var. parviflora but this is rejected as the latter name has no valid publication

Editorial: Toyota Production System practices asFast‐and‐Frugalheuristics

Editorial: Toyota Production System practices as<scp>Fast‐and‐Frugal</scp>heuristics

Decisions to leave: a semi‐fictional essay

Critical QuarterlyEarly View Original Article Decisions to leave: a semi-fictional essay Vanessa Lim, Corresponding Author Vanessa Lim [email protected] orcid.org/0000-0002-8276-245X Seoul National University, London Corresponding author Vanessa Lim, Department of English Language and Literature, Seoul National University, 1 Gwanak-gu, Gwanak-ro, Seoul, South Korea. Email: [email protected]Search for more papers by this author Vanessa Lim, Corresponding Author Vanessa Lim [email protected] orcid.org/0000-0002-8276-245X Seoul National University, London Corresponding author Vanessa Lim, Department of English Language and Literature, Seoul National University, 1 Gwanak-gu, Gwanak-ro, Seoul, South Korea. Email: [email protected]Search for more papers by this author First published: 08 May 2023 https://doi.org/10.1111/criq.12721Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Early ViewOnline Version of Record before inclusion in an issue RelatedInformation

Task offloading strategy and scheduling optimization for internet of vehicles based on deep reinforcement learning

Driven by the construction of smart cities, networks and communication technologies are gradually infiltrating into the Internet of Things (IoT) applications in urban infrastructure, such as automatic driving. In the Internet of Vehicles (IoV) environment, intelligent vehicles will generate a lot of data. However, the limited computing power of in-vehicle terminals cannot meet the demand. To solve this problem, we first simulate the task offloading model of vehicle terminal in Mobile Edge Computing (MEC) environment. Secondly, according to the model, we design and implement a MEC server collaboration scheme considering both delay and energy consumption. Thirdly, based on the optimization theory, the system optimization solution is formulated with the goal of minimizing system cost. Because the problem to be resolved is a mixed binary nonlinear programming problem, we model the problem as a Markov Decision Process (MDP). The original resource allocation decision is turned into a Reinforcement Learning (RL) problem. In order to achieve the optimal solution, the Deep Reinforcement Learning (DRL) method is used. Finally, we propose a Deep Deterministic Policy Gradient (DDPG) algorithm to deal with task offloading and scheduling optimization in high-dimensional continuous action space, and the experience replay mechanism is used to accelerate the convergence and enhance the stability of the network. The simulation results show that our scheme has good performance optimization in terms of convergence, system delay, average task energy consumption and system cost. For example, compared with the comparison algorithm, the system cost performance has improved by 9.12% under different task sizes, which indicates that our scheme is more suitable for highly dynamic Internet of Vehicles environment.

Translation and cultural adaptation of MedStopper®-A web-based decision aid for deprescribing in older adults: A protocol.

Older patients are more likely to have medication-related problems, which are associated with changes in pharmacokinetics and pharmacodynamics, multimorbidity, and polypharmacy. Polypharmacy and inappropriate prescribing are well-known risk factors which commonly cause adverse clinical outcomes in older people. Prescribers struggle to identify potentially inappropriate medications and to choose an adequate tapering approach. The goal of the study is to translate and culturally adapt MedStopper®, an original English language web-based decision aid system in deprescribing medication, to the Portuguese population. A translation-back translation method, with validation of the obtained Portuguese version of MedStopper® will be used, followed by a comprehension test. This is the first research in the Portuguese primary care setting that aims to provide a useful online tool for the appropriate prescription of older patients. The translated version in Portuguese version of the MedStopper® tool will represent an advance that seeks to continue improving the management of medications in the elderly. The adaptation into Portuguese of the educational tool provides clinicians with a screening tool to detect potentially inappropriate prescribing in patients older than 65 that reliable and easier to use. Retrospectively registered.

Understanding Motivational Factors in Social Media News Sharing Decisions

News sharing has become prevalent on many social media platforms. Users are not only exposed to news shared by others, but also actively share information with a diverse set of motivations. In this work, we propose five news sharing motivations based on the intrinsic and extrinsic factors found in prior literature. Through an online experiment, we further examine how a host of factors, including motivations, influence participants' decision to share news online. We then prompt participants to switch their original decision for extra compensation, observing how different news types, motivational and demographic factors may affect the switch. Our analysis suggests that sharing decisions can be reversed when a strong external stimulus (higher bonus) is presented. Further, there are motivational factors that independently influence participants' reversal decisions. Finally, using our work as an empirical basis, we propose designs for future new sharing systems.

On Learning and Testing of Counterfactual Fairness through Data Preprocessing

Machine learning has become more important in real-life decision-making but people are concerned about the ethical problems it may bring when used improperly. Recent work brings the discussion of machine learning fairness into the causal framework and elaborates on the concept of Counterfactual Fairness. In this article, we develop the Fair Learning through dAta Preprocessing (FLAP) algorithm to learn counterfactually fair decisions from biased training data and formalize the conditions where different data preprocessing procedures should be used to guarantee counterfactual fairness. We also show that Counterfactual Fairness is equivalent to the conditional independence of the decisions and the sensitive attributes given the processed nonsensitive attributes, which enables us to detect discrimination in the original decision using the processed data. The performance of our algorithm is illustrated using simulated data and real-world applications. Supplementary materials for this article are available online.

CRIMINAL LAW ARRANGEMENTS IN INDONESIA RELATED TO JUDICIAL REVIEW

Legal remedies for judicial review without being preceded by ordinary legal remedies are a phenomenon that is often and increasingly occurring in Indonesia's justice world. It phenomenon occurs in order to take advantage of legal loopholes. The convict immediately submits a legal remedy for judicial review because, in legal proceedings for judicial review, there is a principle that the sentence imposed must not exceed the original decision. Material justice and truth become a discourse on the application of extraordinary legal remedies for judicial review, thus whether the provisions of Article 263 of the Criminal Procedure Code can be applied to court decisions that have obtained permanent legal force without going through appeals and cassation. This study aims to find out the substance of criminal law arrangements in Indonesia related to judicial review. The research method used is normative legal research, namely research conducted by the author using laws and a conceptual approach, legal material collection techniques using library research, then legal material obtained from library research is analyzed qualitatively. The research results obtained are Judicial Review in principle, an extraordinary remedy for court decisions with permanent legal force (inkracht van gewisjde), aims to provide legal certainty and is formed to address the interests of the convict, not the interests of the state or the victim. Judicial review is regulated in Law no. 8 of 1981 concerning Criminal Procedure Code ("KUHAP") with articles including: Article 263, Article 264, Article 265, Article 266, Article 267, Article 268, Article 269.

Abstract 4355: Prediction of cancer treatment response from histopathology images for a broad set of treatments and indications

Abstract Background: Advances in artificial intelligence have paved the way for predicting cancer patients’ survival and response to treatment from hematoxylin and eosin (H&amp;E)-stained tumor slides. Extant approaches do so either via prediction of actionable mutations and gene fusions, or directly from the H&amp;E images by training a task-specific model using large treatment outcome data. Methods: Here we present the first approach for predicting patient response to multiple targeted treatments and immunotherapies directly from H&amp;E slides. It is founded on two conceptual steps: (1) First, we developed DeepPT, a deep-learning framework that trains on formalin-fixed, paraffin-embedded (FFPE) TCGA whole slide images and their corresponding gene expression profiles to predict wide-scale tumor gene expression from the slides. (2) Second, we apply ENLIGHT, a published approach that predicts individual responses to a wide range of targeted and immunotherapies based on the tumor biopsy measured transcriptomics. Here we apply ENLIGHT to predict patient treatment response from the DeepPT predicted expression values instead of those measured directly from the tumor, notably, without any further training or adaptation of ENLIGHT. Results: First, we find that DeepPT generalizes well to predicting gene expression in all 13 TCGA cohorts tested in cross-validation and importantly, in two independent unseen breast and brain cancer datasets. DeepPT outperforms HE2RNA, a state-of-the-art algorithm for the same task. Second, we demonstrate that the combined DeepPT/ENLIGHT pipeline (termed ENLIGHT-DeepPT) successfully predicts true responders using the original ENLIGHT decision threshold with odds ratios of 1.5 - 4.5, increasing the baseline response rates by 15-85% among predicted responders in five independent unseen cohorts of diverse cancer types and treatments. Remarkably, in one dataset where matched data was available, ENLIGHT-DeepPT has similar performance to that obtained by a supervised learning algorithm that was recently published in Nature, trained on the same cohort. Conclusions: We present for the first time a general framework for predicting patient response to a broad array of targeted and checkpoint therapies from histopathological images, without reliance on treatment outcome data, which are yet scarce and challenging to obtain. Importantly, ENLIGHT-DeepPT offers clinicians real-time treatment recommendations when one cannot wait for sequencing results. Also, and due to its very low cost, we very much hope that it will facilitate the advent of precision oncology in developing countries. Citation Format: Danh-Tai Hoang, Gal Dinstag, Leandro C. Hermida, Doreen S. Ben-Zvi, Efrat Elis, Katherine Caley, Sanju Sinha, Neelam Sinha, Christopher H. Dampier, Tuvik Beker, Kenneth Aldape, Ranit Aharonov, Eric A. Stone, Eytan Ruppin. Prediction of cancer treatment response from histopathology images for a broad set of treatments and indications. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4355.

Evaluation of the effectiveness of the decision support algorithm for physicians in retinal dystrophy using machine learning methods

Electroretinography is a method of electrophysiological testing, which allows diagnosing diseases associated with disorders of the vascular structures of the retina. The classical analysis of the electroretinogram is based on assessing four parameters of the amplitude-time representation and often needs to be specified further using alternative diagnostic methods. This study proposes the use of an original physician decision support algorithm for diagnosing retinal dystrophy. The proposed algorithm is based on machine learning methods and uses parameters extracted from the wavelet scalogram of pediatric and adult electroretinogram signals. The study also uses a labeled database of pediatric and adult electroretinogram signals recorded using a computerized electrophysiological workstation EP-1000 (Tomey GmbH) at the IRTC Eye Microsurgery Ekaterinburg Center. The scientific novelty of this study consists in the development of special mathematical and algorithmic software for analyzing a procedure for extracting wavelet scalogram parameters of the electroretinogram signal using the cwt function of the PyWT. The basis function is a Gaussian wavelet of order 8. Also, the scientific novelty includes the development of an algorithm for analyzing electroretinogram signals that implements the classification of adult (pediatric) electroretinogram signals 19 (20) percent more accurately than classical analysis.

Effect of the Extended Focused Assessment With Sonography for Trauma on the Screening Performance of the National Emergency X-Radiography Utilization Study Chest Decision Instrument.

Developed to decrease unnecessary thoracic computed tomography use in adult blunt trauma patients, the National Emergency X-Radiography Utilization Study (NEXUS) Chest clinical decision instrument does not include the extended Focused Assessment with Sonography in Trauma (eFAST). We assessed whether eFAST improves the NEXUS Chest clinical decision instrument's diagnostic performance and may replace the chest radiograph (CXR) as a predictor variable. We performed a secondary analysis of prospective data from 8 Level I trauma centers from 2011-2014. We compared performance of modified clinical decision instruments that (1) added eFAST as a predictor (eFAST-added clinical decision instrument), and (2) replaced CXR with eFAST (eFAST-replaced clinical decision instrument), in screening for blunt thoracic injuries. One thousand nine hundred fifty-seven patients had documented computed tomography, CXR, clinical NEXUS criteria, and adequate eFAST; 624 (31.9%) patients had blunt thoracic injuries, and 126 (6.4%) had major injuries. Compared to the NEXUS Chest clinical decision instrument, the eFAST-added clinical decision instrument demonstrated unchanged screening performance for major injury (sensitivity 0.98 [0.94 to 1.00], specificity 0.28 [0.26 to 0.30]) or any injury (sensitivity 0.97 [0.95 to 0.98], specificity 0.21 [0.19 to 0.23]). The eFAST-replaced clinical decision instrument demonstrated unchanged sensitivity for major injury (sensitivity 0.93 [0.87 to 0.97], specificity 0.31 [0.29 to 0.34]) and decreased sensitivity for any injury (0.93 [0.91 to 0.951] versus 0.97 [0.953 to 0.98]). In our secondary analysis, adding eFAST as a predictor variable did not improve the diagnostic screening performance of the original NEXUS Chest clinical decision instrument; eFAST cannot replace the CXR criterion of the NEXUS Chest clinical decision instrument.