Cultural assimilation of adult Syrian refugees in Lebanon: Effect modification by religiosity and sex on the relation between cultural adversities and common mental health disorders.

OBJECTIVE: In this study, we aimed to explore the prevalence and determinants of common mental health disorders (CMHDs, posttraumatic stress disorder [PTSD], depression, and anxiety) in Syrian refugees in Lebanon. Specifically, we examined how the associations between cultural adversities (discrimination, unemployment, and separation from family) and CMHDs are modified by levels of religiosity and sex. METHOD: Between March and June 2017, a cross-sectional study was conducted targeting adult Arab Syrian refugees residing in Beirut and Southern Lebanon. Eligibility criteria comprised being a United Nations High Commissioner for Refugees-registered Syrian refugee residing in Lebanon, 18 years and older, and having no history of mental disorder or physical disability. A total of 191 refugees agreed to participate and complete a battery of six questionnaires. Exposures were measured using a sociodemographic questionnaire, the Postmigration Living Difficulties Checklist, the Harvard Trauma Questionnaire, and the Belief into Action Scale, while outcomes were measured using the Posttraumatic Stress Disorder Checklist for DSM-5 and the Depression and Anxiety Scale-21 Items. RESULTS: Half (50.3%) of our sample had high PTSD risk, 73.8% had high depression risk, and 73.8% had high anxiety risk. Stratified analysis revealed religiosity and sex to be effect modifiers of the associations between cultural adversities and CMHDs. Specifically, cultural adversities were only significantly associated with CMHDs in the low religiosity stratum and males. Only unemployment was a significant risk factor for PTSD in both males (OR = 4.53, 95% CI [1.44, 14.27]) and females (OR = 2.77, 95% CI [1.14, 6.74]). CONCLUSIONS: Religiosity and sex are effect modifiers of the associations between cultural adversities and CMHDs. Religious and spiritual interventions in mental health care should be adopted in refugee settings. Moreover, there is an urgent need for capacity-building initiatives addressing social determinants of mental health among Syrian refugees in Lebanon. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Swarm Metaverse for Multi-Level Autonomy Using Digital Twins.

Robot swarms are becoming popular in domains that require spatial coordination. Effective human control over swarm members is pivotal for ensuring swarm behaviours align with the dynamic needs of the system. Several techniques have been proposed for scalable human-swarm interaction. However, these techniques were mostly developed in simple simulation environments without guidance on how to scale them up to the real world. This paper addresses this research gap by proposing a metaverse for scalable control of robot swarms and an adaptive framework for different levels of autonomy. In the metaverse, the physical/real world of a swarm symbiotically blends with a virtual world formed from digital twins representing each swarm member and logical control agents. The proposed metaverse drastically decreases swarm control complexity due to human reliance on only a few virtual agents, with each agent dynamically actuating on a sub-swarm. The utility of the metaverse is demonstrated by a case study where humans controlled a swarm of uncrewed ground vehicles (UGVs) using gestural communication, and via a single virtual uncrewed aerial vehicle (UAV). The results show that humans could successfully control the swarm under two different levels of autonomy, while task performance increases as autonomy increases.

Characterization of Indicators for Adaptive Human-Swarm Teaming.

Swarm systems consist of large numbers of agents that collaborate autonomously. With an appropriate level of human control, swarm systems could be applied in a variety of contexts ranging from urban search and rescue situations to cyber defence. However, the successful deployment of the swarm in such applications is conditioned by the effective coupling between human and swarm. While adaptive autonomy promises to provide enhanced performance in human-machine interaction, distinct factors must be considered for its implementation within human-swarm interaction. This paper reviews the multidisciplinary literature on different aspects contributing to the facilitation of adaptive autonomy in human-swarm interaction. Specifically, five aspects that are necessary for an adaptive agent to operate properly are considered and discussed, including mission objectives, interaction, mission complexity, automation levels, and human states. We distill the corresponding indicators in each of the five aspects, and propose a framework, named MICAH (i.e., Mission-Interaction-Complexity-Automation-Human), which maps the primitive state indicators needed for adaptive human-swarm teaming.

Weighted Gate Layer Autoencoders.

A single dataset could hide a significant number of relationships among its feature set. Learning these relationships simultaneously avoids the time complexity associated with running the learning algorithm for every possible relationship, and affords the learner with an ability to recover missing data and substitute erroneous ones by using available data. In our previous research, we introduced the gate-layer autoencoders (GLAEs), which offer an architecture that enables a single model to approximate multiple relationships simultaneously. GLAE controls what an autoencoder learns in a time series by switching on and off certain input gates, thus, allowing and disallowing the data to flow through the network to increase network's robustness. However, GLAE is limited to binary gates. In this article, we generalize the architecture to weighted gate layer autoencoders (WGLAE) through the addition of a weight layer to update the error according to which variables are more critical and to encourage the network to learn these variables. This new weight layer can also be used as an output gate and uses additional control parameters to afford the network with abilities to represent different models that can learn through gating the inputs. We compare the architecture against similar architectures in the literature and demonstrate that the proposed architecture produces more robust autoencoders with the ability to reconstruct both incomplete synthetic and real data with high accuracy.

A model of symbiomemesis: machine education and communication as pillars for human-autonomy symbiosis.

Symbiosis is a physiological phenomenon where organisms of different species develop social interdependencies through partnerships. Artificial agents need mechanisms to build their capacity to develop symbiotic relationships. In this paper, we discuss two pillars for these mechanisms: machine education (ME) and bi-directional communication. ME is a new revolution in artificial intelligence (AI) which aims at structuring the learning journey of AI-enabled autonomous systems. In addition to the design of a systematic curriculum, ME embeds the body of knowledge necessary for the social integration of AI, such as ethics, moral values and trust, into the evolutionary design and learning of the AI. ME promises to equip AI with skills to be ready to develop logic-based symbiosis with humans and in a manner that leads to a trustworthy and effective steady-state through the mental interaction between humans and autonomy; a state we name symbiomemesis to differentiate it from ecological symbiosis. The second pillar, bi-directional communication as a discourse enables information to flow between the AI systems and humans. We combine machine education and communication theory as the two prerequisites for symbiosis of AI agents and present a formal computational model of symbiomemesis to enable symbiotic human-autonomy teaming. This article is part of the theme issue 'Towards symbiotic autonomous systems'.

Drone approach parameters leading to lower stress sheep flocking and movement: sky shepherding.

Consumer groups are pressuring modern farmers to be more efficient with a focus on better animal welfare. Herding risks farmer lives, involves stress from farm dogs, and if not performed often and intelligently, risks neglect. We examined the behavioural and physiological response of twelve Dorper sheep (Ovies aries) to a drone to adapt mathematical models of shepherding to the new dimension. The model aims to make it feasible for artificial intelligence to improve the autonomy of farmers and pilots in shepherding from the sky. Sheep acclimatised quickly and positively to the drone initiating drive of a flock, regardless of drone speed. Our results demonstrate that stimulating sheep auditory awareness during herding from the sky leads to varying sheep responses. When controlled, these auditory cues can maintain safer distances between the drone and the sheep, offering great potential for the agriculture industry. We outline our ongoing research plans to achieve more autonomous sky shepherding that is compassionate to animal welfare and trusted by farmers and the consuming public.

Characterization of Traumatic Brain Injury Research in the Middle East and North Africa Region: A Systematic Review.

OBJECTIVES: Traumatic brain injury (TBI) represents a major health concern worldwide with a large impact in the Middle East and North Africa (MENA) region as a consequence of protracted wars and conflicts that adversely affect the general population. Currently, systematic TBI studies in the MENA region are lacking, nonetheless they are immensely needed to enhance trauma management and increase survival rates among TBI patients. This systematic review aims to characterize TBI in the MENA region to guide future policy choices and research efforts and inform tailored guidelines capable of improving TBI management and patient treatment and outcome. Furthermore, it will serve as a road map to evaluate and assess knowledge of trauma impact on regional health systems that can be adopted by health-care providers to raise awareness and improve trauma care. METHODS: We conducted a comprehensive search strategy of several databases including MEDLINE/Ovid, PubMed, Embase, Scopus, CINAHL, Google Scholar, and the grey literature in accordance with the PROSPERO systematic review protocol CRD42017058952. Abstracts were screened, and selected eligible studies were reviewed independently by 2 reviewers. We collected demographics information along with TBI characteristics, mortality rates, and regional distribution. Data were extracted using REDCap and checked for accuracy. RESULTS: The search strategy yielded 23,385 citations; 147 studies met the eligibility criteria and were included in this review. Motor vehicle accident (MVA) was the leading cause of TBI (41%) in the MENA region, followed by the military- (15.6%) and fall- (8.8%) related TBI. Males predominantly suffer from TBI-related injuries (85%), with a high prevalence of MVA- and military-related TBI injuries. The TBI mortality rate was 12.9%. The leading causes of mortality were MVA (68%), military (20.5%), and assault (2.9%). The vast majority of reported TBI severity was mild (63.1%) compared to moderate (10.7%) and severe TBI (20.2%). Patients mainly underwent a Glasgow Coma Scale assessment (22.1%), followed by computed tomography scan (8.9%) and surgery (4.1%). CONCLUSIONS: Despite its clinical, social, and economic burden, the evidence of TBI research in the MENA region is scarce. Further research and high-quality epidemiological studies are urgently needed to gain a deep understanding of the TBI burden in the region, facilitate the allocation of adequate resources, implement effective preventive and intervention strategies and advise on the TBI patient management as reflective on the TBI patterns and modes.

Laparoscopic Sleeve Gastrectomy under Awake Paravertebral Blockade Versus General Anesthesia: Comparison of Short-Term Outcomes.

AIMS: This study aimed at comparing the pre-, intra-, and early postoperative outcomes, between patients who underwent PVB vs general anesthesia (GA) during LSG. Follow-up of weight loss at least 1 year postoperatively was also evaluated. METHODS: A cohort study was conducted by selecting all patients who underwent LSG under PVB and GA at Makassed General Hospital between 2010 and 2016. Demographic, social, pre-op health status, body mass index (BMI), operative time, postoperative pain and pain medication consumption, postoperative complications and length of hospital stay, all were studied. Follow-up weight loss was collected up to 5 years postoperatively. Data entry, management, and descriptive and inferential statistics were performed using SPSS. RESULTS: A total of 210 participants were included in this study of which 48 constituted the PVB group and 162 patients composed the GA group. Both groups were similar in baseline demographic factors, with patients in PVB suffering from higher number and advanced stage of comorbidities than the GA group. Mean operative time was similar in between the two groups with 80 ± 20 min for PVB and 82 ± 18 min for GA group. Intraoperative complications were scarce among both study groups. GA group requested a second dose of analgesia earlier than PVB group. After at least 1 year postoperatively, the mean percentage of excess weight loss was 81.35 ± 15.5% and 77.89 ± 14.3% for the PVB and GA groups, respectively, P value 0.45. CONCLUSION: Outcomes of LSG under both types of anesthesia (PVB alone and GA alone) were found to be comparable. However, the need for analgesia was significantly less in the PVB group compared to GA group.

Multimodal Fusion for Objective Assessment of Cognitive Workload: A Review.

Considerable progress has been made in improving the estimation accuracy of cognitive workload using various sensor technologies. However, the overall performance of different algorithms and methods remain suboptimal in real-world applications. Some studies in the literature demonstrate that a single modality is sufficient to estimate cognitive workload. These studies are limited to controlled settings, a scenario that is significantly different from the real world where data gets corrupted, interrupted, and delayed. In such situations, the use of multiple modalities is needed. Multimodal fusion approaches have been successful in other domains, such as wireless-sensor networks, in addressing single-sensor weaknesses and improving information quality/accuracy. These approaches are inherently more reliable when a data source is lost. In the cognitive workload literature, sensors, such as electroencephalography (EEG), electrocardiography (ECG), and eye tracking, have shown success in estimating the aspects of cognitive workload. Multimodal approaches that combine data from several sensors together can be more robust for real-time measurement of cognitive workload. In this article, we review the published studies related to multimodal data fusion to estimate the cognitive workload and synthesize their main findings. We identify the opportunities for designing better multimodal fusion systems for cognitive workload modeling.

Characteristics and Impact of U.S. Military Blast-Related Mild Traumatic Brain Injury: A Systematic Review.

As a result of armed conflict, head trauma from exposure to blasts is an increasing critical health issue, particularly among military service members. Whilst numerous studies examined the burden of blast-related brain injuries on service members', few systematic reviews have been published. This work provides a comprehensive summary of the evidence on blast-related mild traumatic brain injury (mTBI) burden in active U.S. military service members and inactive Veterans, describing characteristics and outcomes. Records published up to April 2017 were identified through a search of PubMed, Web of Science, Scopus, Ovid MEDLINE, and Cochrane Library. Records-based and original research reporting on U.S. military service members and Veterans with mild blast TBI were included. Data on subject characteristics, exposure, diagnostic criterion, and outcomes were extracted from included studies using a standardized extraction form and were presented narratively. Of the 2,290 references identified by the search, 106 studies with a total of 37,515 participants met inclusion criteria for blast-related mTBI. All but nine studies were based out of military or Veteran medical facilities. Unsurprisingly, men were over-represented (75-100%). The criteria used to define blast-related mTBI were consistent; however, the methodology used to ascertain whether individuals met those criteria for diagnosis were inconsistent. The diagnosis, most prevalent among the Army, heavily relied on self-reported histories. Commonly reported adverse outcomes included hearing disturbances and headaches. The most frequently associated comorbidities were post-traumatic stress disorder, depression, anxiety, sleep disorders, attention disorders, and cognitive disorders. The primary objective of this review was to provide a summary of descriptive data on blast-related mTBI in a U.S. military population. Low standardization of the methods for reaching diagnosis and problems in the study reporting emphasize the importance to collect high-quality data to fill knowledge gaps pertaining to blast-related mTBI.

The reliability and transparency bases of trust in human-swarm interaction: principles and implications.

Automation reliability and transparency are key factors for trust calibration and as such can have distinct effects on human reliance behaviour and mission performance. One question that remains unexplored is: what are the implications of reliability and transparency on trust calibration for human-swarm interaction? We investigate this research question in the context of human-swarm interaction, as swarm systems are becoming more popular for their robustness and versatility. Thirty-two participants performed swarm-based tasks under different reliability and transparency conditions. The results indicate that trust, whether it is reliability- or transparency-based, indicates high reliance rates and shorter response times. Reliability-based trust is negatively correlated with correct rejection rates while transparency-based trust is positively correlated with these rates. We conclude that reliability and transparency have distinct effects on trust calibration. Practitioner Summary: Reliability and transparency have distinct effects on trust calibration. Findings from our human experiments suggest that transparency is a necessary design requirement if and when humans need to be involved in the decision-loop of human-swarm systems, especially when swarm reliability is high. Abbreviations: HRI: human-robot interaction; IOS: inter-organisational systems; LMM: liner mixed models; MANOVA: multivariate analysis of variance; UxV: heterogeneous unmanned vehicles; UAV: unmanned aerial vehicle.

Electroencephalographic Workload Indicators During Teleoperation of an Unmanned Aerial Vehicle Shepherding a Swarm of Unmanned Ground Vehicles in Contested Environments.

Background: Although many electroencephalographic (EEG) indicators have been proposed in the literature, it is unclear which of the power bands and various indices are best as indicators of mental workload. Spectral powers (Theta, Alpha, and Beta) and ratios (Beta/(Alpha + Theta), Theta/Alpha, Theta/Beta) were identified in the literature as prominent indicators of cognitive workload. Objective: The aim of the present study is to identify a set of EEG indicators that can be used for the objective assessment of cognitive workload in a multitasking setting and as a foundational step toward a human-autonomy augmented cognition system. Methods: The participants' perceived workload was modulated during a teleoperation task involving an unmanned aerial vehicle (UAV) shepherding a swarm of unmanned ground vehicles (UGVs). Three sources of data were recorded from sixteen participants (n = 16): heart rate (HR), EEG, and subjective indicators of the perceived workload using the Air Traffic Workload Input Technique (ATWIT). Results: The HR data predicted the scores from ATWIT. Nineteen common EEG features offered a discriminatory power of the four workload setups with high classification accuracy (82.23%), exhibiting a higher sensitivity than ATWIT and HR. Conclusion: The identified set of features represents EEG indicators for the objective assessment of cognitive workload across subjects. These common indicators could be used for augmented intelligence in human-autonomy teaming scenarios, and form the basis for our work on designing a closed-loop augmented cognition system for human-swarm teaming.

Trust Mediating Reliability-Reliance Relationship in Supervisory Control of Human-Swarm Interactions.

OBJECTIVE: This work aims to further test the theory that trust mediates the interdependency between automation reliability and the rate of human reliance on automation. BACKGROUND: Human trust in automation has been the focus of many research studies. Theoretically, trust has been proposed to impact human reliance on automation by mediating the relationship between automation reliability and the rate of human reliance. Experimentally, however, the results are contradicting as some confirm the mediating role of trust, whereas others deny it. Hence, it is important to experimentally reinvestigate this role of trust and understand how the results should be interpreted in the light of existing theory. METHOD: Thirty-two subjects supervised a swarm of unmanned aerial vehicles (UAVs) in foraging missions in which the swarm provided recommendations on whether or not to collect potential targets, based on the information sensed by the UAVs. By manipulating the reliability of the recommendations, we observed changes in participants' trust and their behavioral responses. RESULTS: A within-subject mediation analysis revealed a significant mediation role of trust in the relationship between swarm reliability and reliance rate. High swarm reliability increased the rate of correct acceptances, but decreased the rate of correct rejections. No significant effect of reliability was found on response time. CONCLUSION: Trust is not a mere by-product of the interaction; it possesses a predictive power to estimate the level of reliance on automation. APPLICATION: The mediation role of trust confirms the significance of trust calibration in determining the appropriate level of reliance on swarm automation.

Network motifs for translator stylometry identification.

Despite the extensive literature investigating stylometry analysis in authorship attribution research, translator stylometry is an understudied research area. The identification of translator stylometry contributes to many fields including education, intellectual property rights and forensic linguistics. In a two stage process, this paper first evaluates the use of existing lexical measures for the translator stylometry problem. Similar to previous research we found that using vocabulary richness in its traditional form as it has been used in the literature could not identify translator stylometry. This encouraged us to design an approach with the aim of identifying the distinctive patterns of a translator by employing network-motifs. Networks motifs are small sub-graphs which aim at capturing the local structure of a complex network. The proposed approach achieved an average accuracy of 83% in three-way classification. These results demonstrate that classic tools based on lexical features can be used for identifying translator stylometry if they get augmented with appropriate non-parametric scaling. Moreover, the use of complex network analysis and network motifs mining provided made it possible to design features that can solve translator stylometry analysis problems.

When is altruistic punishment useful in social dilemmas?

It is not fully understood how cooperation emerges in a population of individuals with no connections or prior experience with each other. Strategy selection that is purely based on accumulated payoffs promotes free riders who put their self interests above that of any group. How could cooperation persist in these settings? Researchers have posited direct or indirect reciprocity as possible explanations but these theories fail if interactions are not repeated or reputations are ignored. Altruistic punishment may provide an answer. Punishers impose a penalty, such as a fine, on defectors. The idea is a sufficiently high enough penalty-or even the threat of a high penalty-will convince defectors that cooperation is more beneficial. The punishment is altruistic because punishers pay a cost to impose a penalty and expect nothing in return (including no future reciprocity). Empirical and human studies have shown when some individuals are punishers, and they are common, cooperation levels tend to increase. But it has never been specified exactly how large this majority of punishers must be to promote cooperation. Here we analyze cooperation, defection and punishment in a social dilemma, public goods game, and precisely identify the necessary conditions to make altruistic punishment an effective strategy for improving group cooperation levels.

Spatio-Spectral Representation Learning for Electroencephalographic Gait-Pattern Classification.

The brain plays a pivotal role in locomotion by coordinating muscles through interconnections that get established by the peripheral nervous system. To date, many attempts have been made to reveal the underlying mechanisms of humans' gait. However, decoding cortical processes associated with different walking conditions using EEG signals for gait-pattern classification is a less-explored research area. In this paper, we design an EEG-based experiment with four walking conditions (i.e., free walking, and exoskeleton-assisted walking at zero, low, and high assistive forces by the use of a unilateral exoskeleton to right lower limb). We proposed spatio-spectral representation learning (SSRL), a deep neural network topology with shared weights to learn the spatial and spectral representations of multi-channel EEG signals during walking. Adoption of weight sharing reduces the number of free parameters, while learning spatial and spectral equivariant features. SSRL outperformed state-of-the-art methods in decoding gait patterns, achieving a classification accuracy of 77.8%. Moreover, the features extracted in the intermediate layer of SSRL were observed to be more discriminative than the hand-crafted features. When analyzing the weights of the proposed model, we found an intriguing spatial distribution that is consistent with the distribution found in well-known motor-activated cortical regions. Our results show that SSRL advances the ability to decode human locomotion and it could have important implications for exoskeleton design, rehabilitation processes, and clinical diagnosis.

Hierarchical Deep Reinforcement Learning for Continuous Action Control.

Robotic control in a continuous action space has long been a challenging topic. This is especially true when controlling robots to solve compound tasks, as both basic skills and compound skills need to be learned. In this paper, we propose a hierarchical deep reinforcement learning algorithm to learn basic skills and compound skills simultaneously. In the proposed algorithm, compound skills and basic skills are learned by two levels of hierarchy. In the first level of hierarchy, each basic skill is handled by its own actor, overseen by a shared basic critic. Then, in the second level of hierarchy, compound skills are learned by a meta critic by reusing basic skills. The proposed algorithm was evaluated on a Pioneer 3AT robot in three different navigation scenarios with fully observable tasks. The simulations were built in Gazebo 2 in a robot operating system Indigo environment. The results show that the proposed algorithm can learn both high performance basic skills and compound skills through the same learning process. The compound skills learned outperform those learned by a discrete action space deep reinforcement learning algorithm.

Assessing Human Judgment of Computationally Generated Swarming Behavior.

Computer-based swarm systems, aiming to replicate the flocking behavior of birds, were first introduced by Reynolds in 1987. In his initial work, Reynolds noted that while it was difficult to quantify the dynamics of the behavior from the model, observers of his model immediately recognized them as a representation of a natural flock. Considerable analysis has been conducted since then on quantifying the dynamics of flocking/swarming behavior. However, no systematic analysis has been conducted on human identification of swarming. In this paper, we assess subjects' assessment of the behavior of a simplified version of Reynolds' model. Factors that affect the identification of swarming are discussed and future applications of the resulting models are proposed. Differences in decision times for swarming-related questions asked during the study indicate that different brain mechanisms may be involved in different elements of the behavior assessment task. The relatively simple but finely tunable model used in this study provides a useful methodology for assessing individual human judgment of swarming behavior.

Degradomics in Neurotrauma: Profiling Traumatic Brain Injury.

Degradomics has recently emerged as a subdiscipline in the omics era with a focus on characterizing signature breakdown products implicated in various disease processes. Driven by promising experimental findings in cancer, neuroscience, and metabolomic disorders, degradomics has significantly promoted the notion of disease-specific "degradome." A degradome arises from the activation of several proteases that target specific substrates and generate signature protein fragments. Several proteases such as calpains, caspases, cathepsins, and matrix metalloproteinases (MMPs) are involved in the pathogenesis of numerous diseases that disturb the physiologic balance between protein synthesis and protein degradation. While regulated proteolytic activities are needed for development, growth, and regeneration, uncontrolled proteolysis initiated under pathological conditions ultimately culminates into apoptotic and necrotic processes. In this chapter, we aim to review the protease-substrate repertoires in neural injury concentrating on traumatic brain injury. A striking diversity of protease substrates, essential for neuronal and brain structural and functional integrity, namely, encryptic biomarker neoproteins, have been characterized in brain injury. These include cytoskeletal proteins, transcription factors, cell cycle regulatory proteins, synaptic proteins, and cell junction proteins. As these substrates are subject to proteolytic fragmentation, they are ceaselessly exposed to activated proteases. Characterization of these molecules allows for a surge of "possible" therapeutic approaches of intervention at various levels of the proteolytic cascade.

Safety of concomitant cholecystectomy at the time of laparoscopic sleeve gastrectomy: analysis of the American College of Surgeons National Surgical Quality Improvement Program database.

BACKGROUND: The indication and safety of concomitant cholecystectomy (CC) during bariatric surgical procedures are topics of controversy. Studies on the outcomes of CC with laparoscopic sleeve gastrectomy (LSG) are scarce. OBJECTIVES: To assess the safety and 30-day surgical outcomes of CC with LSG. METHODS: A retrospective analysis of the American College of Surgeons National Surgical Quality Improvement Program database 2010 to 2013. Univariate and multivariate analyses were used. RESULTS: Between 2010 and 2013, 21,137 patients underwent LSG; of those 422 (2.0%) underwent CC (LSG+CC), and the majority (20,715 [98%]) underwent LSG alone. Patients in both groups were similar in age, sex distribution, baseline weight, and body mass index. The average surgical time was significantly higher, by 33 minutes, in the LSG+CC cohort. No differences were noted between the groups with regard to overall 30-day mortality and length of hospital stay. CC increased the odds of any adverse event (5.7% versus 4.0%), but the difference did not reach statistical significance (odds ratio 1.49, P = .07). Two complications were noted to be significantly higher with LSG+CC, namely bleeding (P = .04) and pneumonia (P = .02). CONCLUSION: CC during LSG appears to be a safe procedure with slightly increased risk of bleeding and pneumonia compared with LSG alone. When factoring the potential risk and cost of further hospitalization for deferred cholecystectomy, these data support CC for established gallbladder disease.