Error Variance Research Articles

Electronic Medical Record Data Missingness and Interruption in Antiretroviral Therapy Among Adults and Children Living With HIV in Haiti: Retrospective Longitudinal Study.

Children (aged 0-14 years) living with HIV often experience lower rates of HIV diagnosis, treatment, and viral load suppression. In Haiti, only 63% of children living with HIV know their HIV status (compared to 85% overall), 63% are on treatment (compared to 85% overall), and 48% are virally suppressed (compared to 73% overall). Electronic medical records (EMRs) can improve HIV care and patient outcomes, but these benefits are largely dependent on providers having access to quality and nonmissing data. We sought to understand the associations between EMR data missingness and interruption in antiretroviral therapy treatment by age group (pediatric vs adult). We assessed associations between patient intake record data missingness and interruption in treatment (IIT) status at 6 and 12 months post antiretroviral therapy initiation using patient-level data drawn from iSanté, the most widely used EMR in Haiti. Missingness was assessed for tuberculosis diagnosis, World Health Organization HIV stage, and weight using a composite score indicator (ie, the number of indicators of interest missing). Risk ratios were estimated using marginal parameters from multilevel modified Poisson models with robust error variances and random intercepts for the facility to account for clustering. Data were drawn from 50 facilities and comprised 31,457 patient records from people living with HIV, of which 1306 (4.2%) were pediatric cases. Pediatric patients were more likely than adult patients to experience IIT (n=431, 33% vs n=7477, 23.4% at 6 months; P<.001). Additionally, pediatric patient records had higher data missingness, with 581 (44.5%) pediatric records missing at least 1 indicator of interest, compared to 7812 (25.9%) adult records (P<.001). Among pediatric patients, each additional indicator missing was associated with a 1.34 times greater likelihood of experiencing IIT at 6 months (95% CI 1.08-1.66; P=.008) and 1.24 times greater likelihood of experiencing IIT at 12 months (95% CI 1.05-1.46; P=.01). These relationships were not statistically significant for adult patients. Compared to pediatric patients with 0 missing indicators, pediatric patients with 1, 2, or 3 missing indicators were 1.59 (95% CI 1.26-2.01; P<.001), 1.74 (95% CI 1.02-2.97; P=.04), and 2.25 (95% CI 1.43-3.56; P=.001) times more likely to experience IIT at 6 months, respectively. Among adult patients, compared to patients with 0 indicators missing, having all 3 indicators missing was associated with being 1.32 times more likely to experience IIT at 6 months (95% CI 1.03-1.70; P=.03), while there was no association with IIT status for other levels of missingness. These findings suggest that both EMR data quality and quality of care are lower for children living with HIV in Haiti. This underscores the need for further research into the mechanisms by which EMR data quality impacts the quality of care and patient outcomes among this population. Efforts to improve both EMR data quality and quality of care should consider prioritizing pediatric patients.

Practical finite‐time and fixed‐time multigroup consensus for second‐order nonlinear multi‐agent systems with Markov switching topology

AbstractPractical finite‐time and fixed‐time multigroup consensus questions for second‐order nonlinear multi‐agent systems (MASs) with Markov switching topologies are investigated in this article. Based on the topology of switching, each agent will appear to switch between different groups, and by setting up group switching signal variables embedded in error variables for each agent, finite‐time and fixed‐time control protocols are proposed, respectively, such that the conditions that must be satisfied for all agents to achieve practical finite‐time multigroup consensus and practical fixed‐time multigroup consensus can be obtained, respectively. For unknown nonlinear functions in the dynamical system, a radial basis function neural network (RBFNN) fit is used for approximation. Correspondingly, two examples are provided to verify the control effects of the control protocols.

An online low-dimension fuzzy modeling method for time-varying processes

Many industrial processes are full of nonlinearity and noise. Fuzzy modeling often leads to issues with dimension disaster and poor robustness for such processes. To address these issues, an online low-dimension fuzzy method (OLDF) is proposed here. First, an explicit mapping-based low-dimension fuzzy method (EMLF) is proposed to handle intricate nonlinear and noisy processes. In this method, similar fuzzy sets are combined into larger ones using a novel similarity criterion to reduce the fuzzy rules. Then, an explicit mapping strategy is developed to represent the local nonlinearities in each enlarged fuzzy set through an explicit mapping function. Subsequently, a robust objective function is constructed to improve the robustness of EMLF model. This objective function minimizes both the mean and variance of the modeling error, ensuring robust performance even in scenarios featuring non-Gaussian noise or outliers. After that, an online update strategy is designed to account for time-varying dynamics. It designs an adaptive detection mechanism to identify changes in dynamic behaviors. Following detection, global and local updating strategies are developed to dynamically update model parameters. Further analysis and proof highlight the effectiveness of the proposed method. Experiments with time-varying processes also confirm its success.

An Adaptive Channel Selection Method for Assimilating the Hyperspectral Infrared Radiances

Abstract Hyperspectral infrared (IR) satellites can provide high-resolution vertical profiles of the atmospheric state, which significantly contributes to the forecast skill of numerical weather prediction, especially for regions with sparse observations. One challenge in assimilating the hyperspectral radiances is how to effectively extract the observation information, due to the interchannel correlations and correlated observation errors. An adaptive channel selection method is proposed, which is implemented within the data assimilation scheme and selects the radiance observation with the maximum reduction of variance in observation space. Compared to the commonly used channel selection method based on the maximum entropy reduction (ER), the adaptive method can provide flow-dependent and time-varying channel selections. The performance of the adaptive selection method is evaluated by assimilating only the synthetic Fengyun-4A (FY-4A) GIIRS IR radiances in an observing system simulation experiment (OSSE), with model resolutions from 7.5 to 1.5 km and then 300 m. For both clear-sky and all-sky conditions, the adaptive method generally produces smaller RMS errors of state variables than the ER-based method given similar amounts of assimilated radiances, especially with fine model resolutions. Moreover, the adaptive method has minimum RMS errors smaller than or approaching those with all channels assimilated. For the intensity of the tropical cyclone, the adaptive method also produces smaller errors of the minimum dry air mass and maximal wind speed at different levels, compared to the ER-based selection method. Significance Statement Assimilating satellite radiances has been essential for numerical weather prediction. Hyperspectral infrared satellites provide high-resolution vertical profiles for the atmospheric state and can further improve the numerical weather prediction. Due to limited computational resources, and correlated observations and associated errors, efficient and effective ways to assimilate the hyperspectral radiances are needed. An adaptive channel selection method that is incorporated with data assimilation is proposed. The adaptive channel selection can effectively extract the information from hyperspectral radiances under both clear- and all-sky conditions, with increased model resolutions from kilometers to subkilometers.

Reply to Comment by W. Knoben and M. Clark on “The Treatment of Uncertainty in Hydrometric Observations: A Probabilistic Description of Streamflow Records”

AbstractIn this Reply, we address the concerns of Knoben and Clark (2023, https://doi.org/10.1029/2022WR034294) or KC23 that “the assumptions needed to effectively use difference‐based variance estimation methods are not always met by hourly streamflow records.” There should be little doubt that the assumptions of our difference‐based estimator will sometimes be violated in hourly streamflow records but the results from de Oliveira and Vrugt (2022, https://doi.org/10.1029/2022wr032263) and confirmed by the findings in our Reply show that such violations are sporadic enough not to affect much the error variance estimates. Snowmelt as pointed out by KC23 (https://doi.org/10.1029/2022WR034294) may not have received sufficient attention in our paper, yet their 365‐day record is simply not long enough to demonstrate bias of our discharge error variance estimates (and their dependence on flow level). This would require analysis of a much longer, multi‐year, streamflow record of a snowmelt‐driven watershed. The snowmelt catchment analyzed in dOV22 (https://doi.org/10.1029/2022wr032263) did not demonstrate bias in the discharge error variance estimates. We also provide additional clarification on the interpretation of the variance estimates obtained with the nonparametric estimator, and discuss the main issues in the test case presented in Knoben and Clark (2023, https://doi.org/10.1029/2022WR034294)).

Connectedness rating among commercial pig breeding herds in Korea.

This study aims to estimate the connectedness rating (CR) of Korean swine breeding herds. Using 104,380 performance and 83,200 reproduction records from three swine breeds (Yorkshire, Landrace and Duroc), the CR was estimated for two traits: average daily gain (ADG) and number born alive (NBA) in eight breeding herds in the Republic of Korea (hereafter, Korea). The average CR for ADG in the Yorkshire breed ranges from 1.32% to 28.5% depending on the farm. The average CR for NBA in the Yorkshire herd ranges from 0% to 12.79%. A total of 60% of Yorkshire and Duroc herds satisfied the preconditions suggested for genetic evaluation among the herds. The precondition for the genetic evaluation of CR for ADG, as a productive trait, was higher than 3% and that of NBA, as a reproductive trait, was higher than 1.5%. The ADG in the Yorkshire herds showed the highest average CR. However, the average CR of ADG in the Landrace herds was lower than the criterion of the precondition. The prediction error variance of the difference (PEVD) was employed to assess the validation of the CR, as PEVDs exhibit fluctuations that are coupled with the CR across the herds. A certain degree of connectedness is essential to estimate breeding value comparisons between pig herds. This study suggests that it is possible to evaluate the genetic performance together for ADG and NBA in the Yorkshire herds since the preconditions were satisfied for these four herds. It is also possible to perform a joint genetic analysis of the ADG records of all Duroc herds since the preconditions were also satisfied. This study provides new insight into understanding the genetic connectedness of Korean pig breeding herds. CR could be utilized to accelerate the genetic progress of Korean pig breeding herds.

Measurement invariance of a Posttraumatic Stress Disorder symptoms measure (PCL-5) in college student and Amazon's mechanical TURK samples.

College student and Amazon's Mechanical TURK (MTURK) samples are regularly utilized in trauma research. Recent literature, however, has criticized these samples for not being generalizable to the general U.S. The purpose of this study was to determine whether college student (n = 255) and MTURK (n = 316) samples are invariant on the Posttraumatic Stress Disorder Checklist for DSM-5. Measurement invariance using confirmatory factor analyses was used to determine whether groups are invariant across factor structure, factor loadings, item intercepts, and residual error variances on a given measure of Post-traumatic Stress Disorder (PTSD) symptom severity. Model fit indices indicated the seven-factor Hybrid model was the best-fitting model, but the six-factor Anhedonia model was the most parsimonious model. Both models demonstrated equivalence in factor at the strictest level, indicating MTURK and college student samples are similar in regard to PTSD symptom severity. These findings provide evidence that these groups can be combined in future studies to increase sample size for trauma research. Only the Anhedonia factor exhibited mean differences between groups, which may be related to true differences between college students and MTURK survey-takers. This study provides further evidence that the findings from trauma studies using these populations are generalizable to each other. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Predictors of One-Year Persistence of Harm From a Known Person's Drinking: Findings From a Longitudinal Population-Based Study in Sweden.

Few studies have investigated the persistence over time of experiences of harm from a known person's drinking. The aim of this study was to describe 1-year persistence and investigate its predictors at baseline. Potential predictors included the harmed person's sociodemographic factors, their own drinking habits, their relationship to the person causing harm, and the type of negative experience. The sample included respondents who reported experiencing harm from a known person's drinking in a general population survey in 2013 and participated in a follow-up survey in 2014 (n = 1,203). One-year persistence of experienced harm was defined as reporting any harm in both surveys. We investigated potential baseline predictors of persistence by estimating relative risks and 95% confidence intervals through modified binary Poisson regression analyses with robust error variance. One-year persistence of experiencing any type of harm from a known person's drinking was seen in 52.5% of those reporting harm at baseline and was higher among women (56.7%) than men (43.3%). Reporting harm from a partner, parent, or other household member significantly predicted 1-year persistence, as did severity of overall perceived harm at baseline. In Sweden, 1-year persistence of experienced harm from a known person's drinking is common in most individuals. The risk of experiencing such harm is higher among women, when harm occurs within close relationships, and when it is experienced as severe at baseline. Support to individuals in these situations is crucial.

Multifaceted validity analysis of clinical skills test in the educational field setting

Introduction: The importance of clinical skills training in traditional Korean medicine education is increasingly emphasized. Since the clinical skills tests are high-stakes tests that determine success in national licensing exams, it is essential to develop reliable multifaceted analysis methods for clinical skills tests in actual education settings. In this study, we applied the multifaceted validity evaluation methods to the evaluation results of the cardiopulmonary resuscitation module to confirm the applicability and effectiveness of the methods.Methods: In this study, we used internal consistency, factor analysis, generalizability theory G-study and D-study, ANOVA, Kendall's tau, descriptive statistics, and other statistical methods to analyze the multidimensional validity of a cardiopulmonary resuscitation test in clinical education settings over the past three years.Results: The factor analysis and internal consistency analysis showed that the evaluation rubric had an unstable structure and low concordance. The G-study showed that the error of the clinical skills assessment was large due to the evaluator and unexpected errors. The D-study showed that the variance error of the evaluator should be significantly reduced to validate the evaluation. The ANOVA and Kendall's tau confirmed that evaluator heterogeneity was a problem.Discussion and Conclusion: Clinical skills tests should be continuously evaluated and managed for validity in two steps of pre-production and actual implementation. This study has presented specific methods for analyzing the validity of clinical skills training and testing in actual education settings. This study would contribute to the foundation for competency-based evidence-based education in practical clinical training.

Food Insecurity Is High in a Multi-Site Cohort of Transgender Women Vulnerable to or Living with HIV in the Eastern and Southern United States: Baseline Findings from the LITE Cohort.

The prevalence and correlates of food insecurity-the unavailability of food and limited access to it-have not been adequately considered among transgender women (TW), particularly alongside other health-related conditions burdening this population, such as HIV infection. This study examined the prevalence and correlates of food insecurity among TW. Between 2018 and 2020, 1590 TW in the Eastern and Southern U.S. completed a multi-site baseline assessment (socio-behavioral survey and HIV testing). Descriptive statistics were calculated and multivariable Poisson models with robust error variance were used to estimate prevalence ratios and 95% confidence intervals for correlates of food insecurity (dichotomized as sometimes-to-always vs. seldom-to-never running out of food). Eighteen percent of TW were living with HIV and nearly half of participants (44%) reported food insecurity. Correlates of food insecurity included being Black, multiracial, or another race/ethnicity; having < college education, low income, unstable housing, and high anticipated discrimination; and a history of sex work and sexual violence (all p < 0.05). Food insecurity was highly prevalent among TW. Current programs to provide food support do not adequately meet the needs of TW. HIV pr evention and care programs may benefit from addressing food insecurity.

Estimating and Calibrating Markov Chain Sample Error Variance

Markov chain Monte Carlo (MCMC) methods are a powerful and versatile tool with applications spanning a wide spectrum of fields, including Bayesian inference, computational biology, and physics. One of the key challenges in applying MCMC algorithms is to deal with estimation error. The main result in this article is a closed form, non-asymptotic solution for the sample error variance of a single MCMC&amp;nbsp; estimate. Importantly, this result assumes that the state-space is finite and discrete. We demonstrate with examples how this result can help estimate and calibrate MCMC estimation error variance in the more general case, when the state-space is continuous and/or unbounded.

Short‐term load and spinning reserve prediction based on LSTM and ANFIS with PSO algorithm

AbstractWith the increase in population and the growth of technology, the load demand has increased and major changes in spinning reserve are unavoidable. Short‐term forecasting to hourly predict the required load and spinning reserve is of great importance. All of the power system studies in planning and operation fields are depend on short‐term hourly load forecasting. In this work, the problem of load forecasting and spinning reserve based on deep learning (DL) algorithms and traditional methods is investigated with the help of the proposed information combination system. The proposed method tries to reduce the weaknesses of the stated methods and increase the accuracy of the predicted signal. First, short‐term predicting of load and spinning reserve is performed using a combination of adaptive network‐based fuzzy inference system (ANFIS) and meta‐heuristic algorithms including differential evolution (DE), genetic algorithm (GA), and particle swarm optimization (PSO). The ANFIS‐PSO is selected as the best ANFIS combination in load and spinning reserve prediction with a lower error criterion than other methods. Also, the long short‐term memory (LSTM) network can provide good accuracy for load and spinning reserve forecasting. Therefore, the combination of ANFIS‐PSO and LSTM is used to reduce the average error and error variance.

Comprehensive knowledge about HIV/AIDS and associated factors among reproductive age women in Liberia

IntroductionAcquired Immune Deficiency Syndrome (AIDS) continues to be a deadly pandemic and a serious threat to public health. Globally, reproductive age women are more likely to be infected with Human Immunodeficiency Virus (HIV). Comprehensive knowledge about HIV/AIDS is pivotal in the fight against AIDS. However, comprehensive HIV/AIDS knowledge is low in Sub-Saharan African (SSA) nations including Liberia, which contributes to the high incidence of HIV in these nations. This study assessed the level of comprehensive knowledge about HIV/AIDS and its associated factors among reproductive age women in Liberia.MethodsThe prevalence and associated factors of comprehensive knowledge about HIV/AIDS among reproductive age women in Liberia were determined using secondary data analysis of 2019–2020 Liberia Demographic and Health Surveys (LDHS). Comprehensive knowledge about HIV/AIDS was a composite variable computed from six variables available in the Demographic and Health Survey (DHS). The study included 7,621 reproductive age women in weighted samples. A generalized linear mixed model with robust error variance was used. For the variables included in the final model, adjusted prevalence ratios (aPR) with 95% confidence intervals (CI) were calculated.ResultsThe prevalence of comprehensive HIV/AIDS knowledge among Liberian women aged 15–49 was 33.5%. Women’s age and education, and distance to health facility were positively associated with comprehensive knowledge about HIV/AIDS among Liberian reproductive age women. In contrast, community poverty level was negatively associated with comprehensive knowledge about HIV/AIDS.Conclusion and recommendationsThis study demonstrates that the prevalence of good comprehensive HIV/AIDS knowledge was relatively low among reproductive age women in Liberia. Hence, health practitioners and policymakers should strengthen HIV/AIDS sensitization programmes to increase women’s knowledge about HIV/AIDS.

Modal identification with uncertainty quantification of large‐scale civil structures via a hybrid operation modal analysis framework

SummaryIn operational modal analysis (OMA), only structural responses are typically available. In this context, bias and variance (uncertainty) errors may exist in modal estimates (especially damping estimates), resulting in inaccurate determination of the modal properties of large‐scale structures under harsh excitations. To this end, a hybrid OMA framework based on the modal decoupling, the natural excitation technique, the random decrement technique (RDT), and improved eigensystem realization algorithm (ERA) with the automated stabilization diagram is presented to perform high‐accuracy modal estimates with uncertainty quantification for large‐scale structures under normal and severe ambient excitations. The accuracy and effectiveness of the hybrid framework for identifying the modal parameters are validated by numerical simulation study of a framework structural model. Furthermore, the hybrid framework is applied to analyze recorded acceleration responses of a supertall building with 600‐m height under normal excitations and typhoon condition to verify its applicability in field measurements. The numerical simulation and field measurement studies demonstrate that the hybrid framework can not only perform precise modal estimations with uncertainty quantification through a single ambient vibration measurement but also effectively reveal the variations of modal properties of supertall structures under harsh excitations from multiple perspectives. This paper aims to enhance the reliability and accuracy of modal estimation for engineering structures and further provide insight into the variations of dynamic properties of large‐scale civil structures under severe excitations.

Negative effects of undernutrition on sputum smear conversion and treatment success among retreatment cases in Uganda: A quasi-experimental study

RationaleThe causal relationship between undernutrition and response to anti-tuberculosis (TB) treatment and TB treatment outcomes among people with retreatment TB is understudied. ObjectiveTo evaluate the effect of undernutrition on treatment success and sputum smear conversion among people with retreatment drug-susceptible TB in Kampala, Uganda. MethodsWe conducted a quasi-experimental study utilizing propensity score weighting among people with retreatment drug-susceptible TB aged ≥ 15 years treated between 2012 and 2022 in Kampala. The primary exposure was undernutrition assessed using the mid-upper arm circumference at the time of TB diagnosis. The primary outcome was treatment success defined as cure or treatment completion at month 6. Sputum smear conversion was the secondary outcome and was measured as a change in sputum smear status from positive to negative at months 2, 5, and 6. We estimated the causal effect of undernutrition on the outcomes using a propensity-score weighted modified Poisson regression model with robust error variance. Measurements and main resultsOf the 605 participants, 432 (71.4 %) were male, 215 (35.5 %) were aged 25–34 years, 427 (70.6 %) had bacteriologically confirmed pulmonary TB, 133 (22.0 %) were undernourished and 398 (65.8 %) achieved treatment success. Of participants with bacteriologically confirmed pulmonary TB, 232 (59.0 %), 327 (59.3 %), and 360 (97.6 %) achieved sputum smear conversion at months 2, 5, and 6, respectively. Undernutrition reduced treatment success (RR 0.42, 95 % CI 0.32–0.55) as well as sputum smear conversion at months 2 (RR 0.45, 95 % CI 0.42–0.49) and 5 (RR 0.46, 95 % CI 0.43–0.51) but not month 6 (RR 0.99, 95 % CI 0.97–1.02). ConclusionUndernutrition negatively impacts treatment outcomes. Therefore, nutritional assessment should be an integral component of TB care, with nutritional counseling and support offered to those undernourished to optimize their TB treatment response and outcomes.

Multi-User System for Earth Sensing Spacecraft Attitude Calibration and Analysis

The Teledyne Multiple User System for Earth Sensing (“MUSES”) was developed for commercial Earth imaging and instrument technology development on the International Space Station (ISS). MUSES simultaneously hosts up to four Earth ensing instruments and provides inertial pointing and tracking to orient instruments toward planned targets on the ground. Up to four instruments are robotically installed on MUSES and periodically interchanged as warranted. MUSES was delivered to the ISS in June 2017, with initial testing successfully completed and subsequently documented in an initial requirements verification report. Assessing the performance of the MUSES attitude estimation system is challenging due to the lack of an independent attitude estimate of the platform attitude. A novel approach is introduced herein where the ISS attitude estimate, derived from ISS Global Positioning System sensors, is employed as an independent measure of MUSES platform attitude. Spectral decomposition methods are used to account for the contributions of ISS deformations to the variance in disparities between the ISS and MUSES attitudes and thus enable a less conservative estimate of the variance in MUSES attitude error. Implementing the spectral decomposition results in a best estimate upper bound on MUSES attitude errors of 34.5 and 27.7 arcsec, respectively, in the along-track and cross-track axes. Both the simulated and real results obtained from the spacecraft are presented.

Bond market spillover networks of ASEAN-4 markets: Is the global pandemic different?

This study explores the interconnectedness of bond markets in the Association of Southeast Asian Nations (ASEAN-4: Indonesia, Malaysia, the Philippines, and Thailand) with significant regional and global markets, focusing on transmitting global shocks to these markets. The analysis is based on the forecast error variance decomposition (FEVD) of a vector autoregression (VAR) model and wavelet-based longer horizon approaches, enabling us to identify the net transmitter and receiver of return and volatility shocks among the underlying markets at various investment horizons. In addition, we evaluate the primary drivers of ASEAN-4 bond markets, employing global and ASEAN-4 macroeconomic indicators and uncertainty measures to explain variations in the spillover dynamics. Empirical findings reveal strong inter-country connectedness among bond markets, with the U.S. market having the most significant links for 7- and 10-year bond maturities. However, there is limited connectedness within ASEAN-4 and other developing countries, indicating strong diversification potential. Furthermore, our findings indicate increased interconnectedness during COVID-19 and that macroeconomic fundamentals significantly influence the ASEAN-4 markets, rationalizing the heterogeneous cross-border transmission of uncertainty shocks. The findings contribute to the literature on bond market spillovers, offering valuable insights for policymakers, regulators, and investors seeking portfolio diversification, risk management, and sustainable growth in the ASEAN-4 markets.

A variance-constrained method to encoding–decoding [formula omitted] state estimation for memristive neural networks with energy harvesting sensor

This paper considers the encoding–decoding-based H∞ state estimation issue under variance constraint for delayed memristive neural networks with energy harvesting sensor. In order to improve the transmission security, the encoding–decoding communication mechanism is introduced in the transmission channel, which can encode the transmitted data with limited bits. In addition, the energy level of the energy harvester is described by a random variable that obeys the specific probability distribution, in which the energy harvesting technology is used to provide the required energy and continuously maintain the operation of the sensor. The main objective is to present new encoding–decoding-based H∞ state estimation method such that, in the presence of time-delay and energy harvesting mechanism, the desirable H∞ performance requirement and the estimation error variance constraints are both ensured by providing some sufficient conditions. In the end, the feasibility of new encoding–decoding-based H∞ state estimation algorithm is demonstrated by a simulation experiment.

Benefits of First Pass Recanalization in Basilar Strokes Based on Initial Clinical Severity.

Randomized trials demonstrating the benefits of thrombectomy for basilar artery occlusions have enrolled an insufficient number of patients with a National Institutes for Health Stroke Scale (NIHSS) score < 10 and shown discrepant results for patients with an NIHSS > 20. Achieving afirst pass recanalization (FPR) improves clinical outcomes in stroke. We aimed to evaluate the effect of the FPR on outcomes among basilar artery occlusion patients, characterized by prethrombectomy initial NIHSS score. We retrospectively analyzed the Endovascular Treatment in Ischemic Stroke (ETIS) registry of 279 basilar artery occlusion patients treated with thrombectomy from 6 participating centers. We compared the 90-day clinical outcomes of achieving aFPR versus no FPR, categorized by initial clinical severity: mild (NIHSS < 10), moderate (NIHSS 10-20) and severe (NIHSS > 20). We used Poisson regression with robust error variance to determine the effect of the NIHSS score on the association between FPR and outcomes. The FPR patients with NIHSS < 10 or NIHSS 10-20 were more likely to have afavorable clinical outcome (modified Rankin scale, mRS 0-3) than non-FPR patients (relative risk, RR = 1.32, 95% confidence interval, CI: 1.04, 1.66, p-value = 0.0213, and RR = 1.79, 95% CI: 1.26, 2.53, p-value = 0.0011, respectively). Asimilar benefit was not found in patients with severe symptoms. We found asignificantly lower risk of poor clinical outcome (mRS 4-6) in FPR patients with NIHSS 10-20, but not among patients with an NIHSS > 20. Achieving aFPR in basilar artery occlusion patients with mild (NIHSS < 10) or moderate (NIHSS 10-20) symptoms is associated with better clinical outcomes, but not in patients with severe symptoms. These results support the importance of further clinical trials on the benefits of thrombectomy in severe strokes.

Connectedness and risk spillover in China's commodity futures sectors

AbstractThis study employs minimum spanning tree and generalized forecast error variance decomposition methods to investigate the connectedness and risk spillovers across China's commodity sectors from January 2016 to December 2021. The results show that total connectedness within the commodity system is time varying. Chemical is the main risk driver, while other sectors occasionally dominate the system. These two methods achieve consistent results in identifying the systemically important sector and dynamic connectedness. In addition, we find that Chinese economic policy uncertainty and the investor sentiment index have significant impacts on total connectedness. Our findings have implications for preventing systemic risk for policymakers and managing commodity portfolio risk for investors.