Impacts of DRG-Based Prepayment Reform on the Cost and Quality of Patients with Neurologic Disorders: Evidence from a Quasi-Experimental Analysis in Beijing, China.

Purpose: As one of the pioneering pilot cities in China's extensive Diagnosis Related Groups (DRG) -based prepayment reform, Beijing is leading a comprehensive overhaul of the prepayment system, encompassing hospitals of varying affiliations and tiers. This systematic transformation is rooted in extensive patient group data, with the commencement of actual payments on March 15, 2022. This study aims to evaluate the effectiveness of DRG payment reform by examining how it affects the cost, volume, and utilization of care for patients with neurological disorders. Patients and Methods: Utilizing the exogenous shock resulting from the implementation of the DRG-based prepayment system, we adopted the Difference-in-Differences (DID) approach to discern changes in outcome variables among DRG payment cases, in comparison to control cases, both before and following the enactment of the DRG policy. The analytical dataset was derived from patients diagnosed with neurological disorders across all hospitals in Beijing that underwent the DRG-based prepayment reform. Strict data inclusion and exclusion criteria, including reasonableness tests, were applied, defining the pre-reform timeframe as March 15th through October 31st, 2021, and the post-reform timeframe as the corresponding period in 2022. The extensive dataset encompassed 53 hospitals and encompassed hundreds of thousands of cases. Results: The implementation of DRG-based prepayment resulted in a substantial 12.6% decrease in total costs per case and a reduction of 0.96 days in length of stay. Additionally, the reform was correlated with significant reductions in overall in-hospital mortality and readmission rates. Surprisingly, the study unearthed unintended consequences, including a significant reduction in the proportion of inpatient cases classified as surgical patients and the Case Mix Index (CMI), indicating potential strategic adjustments by providers in response to the introduction of DRG payments. Conclusion: The DRG payment reform demonstrates substantial effects in restraining cost escalation and enhancing quality. Nevertheless, caution must be exercised to mitigate potential issues such as patient selection bias and upcoding.

Algorithm for community security risk assessment and influencing factors analysis by back propagation neural network.

This paper aims to accurately assess and effectively manage various security risks in the community and overcome the challenges faced by traditional models in handling large amounts of features and high-dimensional data. Hence, this paper utilizes the back propagation neural network (BPNN) to optimize the security risk assessment model. A key challenge of researching community security risk assessment lies in accurately identifying and predicting a range of potential security threats. These threats may encompass natural disasters, public health crises, accidents, and social security issues. The intricate interplay of these risk factors, combined with the dynamic nature of community environments, presents difficulties for traditional risk assessment methodologies to address effectively. Initially, this paper delves into the factors influencing safety incidents within communities and establishes a comprehensive system of safety risk assessment indicators. Leveraging the adaptable and generalizable nature of the BPNN model, the paper proceeds to optimize the BPNN model, enhancing the security risk assessment model through this optimization. Subsequent comparison experiments with traditional models validate the rationality and effectiveness of the proposed model, with hidden layer nodes set at various levels like 10, 15, 20, 25, 30, and 35. These traditional models include Convolutional Neural Network (CNN), Long Short-Term Memory Network (LSTM), Bidirectional Encoder Representations from Transformers (BERT), Generative Pre-trained Transformer (GPT), and eXtreme Gradient Boosting (XGBOOST). Experimental findings demonstrate that with 20 hidden layer nodes, the optimized model achieves a remarkable final recognition accuracy of 99.1 %. Moreover, the optimized model exhibits significantly lower final function loss compared to models with different node numbers. Increasing the number of hidden layer nodes may diminish the optimized model's fit and accuracy. Comparison with traditional models reveals that the average accuracy of the optimized model in community risk identification reaches 98.5 %, with a maximum accuracy of 99.6 %. This marks an improvement of 9%-11 % in recognition accuracy across various risk factors compared to traditional models. Regarding system response time and resource utilization, the optimized model exhibits a response time ranging from 100 ms to 120 ms and consistently lower resource utilization rates across all scenarios, underscoring its efficiency in community security risk assessment. In conclusion, this experiment sheds light on the underlying mechanisms and patterns of community safety risk formation, offering novel perspectives and methodologies for researching community safety risk assessment. The paper concludes by presenting recommendations and strategies for addressing community safety risks based on experimental analysis.

Dynamic assessment of community resilience in China: empirical surveys from three provinces.

Background: Strengthening the construction of community resilience and reducing disaster impacts are on the agenda of the Chinese government. The COVID-19 pandemic could alter the existing community resilience. This study aims to explore the dynamic change trends of community resilience in China and analyze the primary influencing factors of community resilience in the context of COVID-19, as well as construct Community Resilience Governance System Framework in China. Methods: A community advancing resilience toolkit (CART) was used to conduct surveys in Guangdong, Sichuan, and Heilongjiang provinces in China in 2015 and 2022, with community resilience data and information on disaster risk awareness and disaster risk reduction behaviors of residents collected. The qualitative (in-depth interview) data from staffs of government agencies and communities (n = 15) were pooled to explore Community Resilience Governance System Framework in China. Descriptive statistics analysis and t-tests were used to investigate the dynamic development of community resilience in China. Hierarchical regression analysis was performed to explore the main influencing factors of residential community resilience with such socio-demographic characteristics as gender and age being controlled. Results: The results indicate that community resilience in China has improved significantly, presenting differences with statistical significance (p < 0.05). In 2015, connection and caring achieved the highest score, while disaster management achieved the highest score in 2022, with resources and transformative potential ranking the lowest in their scores in both years. Generally, residents presented a high awareness of disaster risks. However, only a small proportion of residents that were surveyed had participated in any "community-organized epidemic prevention and control voluntary services" (34.9%). Analysis shows that core influencing factors of community resilience include: High sensitivity towards major epidemic-related information, particular attention to various kinds of epidemic prevention and control warning messages, participation in epidemic prevention and control voluntary services, and formulation of epidemic response plans. In this study, we have constructed Community Resilience Governance System Framework in China, which included community resilience risk awareness, community resilience governance bodies, community resilience mechanisms and systems. Conclusion: During the pandemic, community resilience in China underwent significant changes. However, community capital was, is, and will be a weak link to community resilience. It is suggested that multi-stages assessments of dynamic change trends of community resilience should be further performed to analyze acting points and core influencing factors of community resilience establishment at different stages.

Research on resilience assessment method of train control on-board system.

To address the problem of difficult performance assessment of train control on-board system after recovery from failures, we have proposed a resilience assessment methodology that uses reliability as an indicator of system resilience. Since the system failures are time-dependent, we adopted the Discrete Time Bayesian Network method to obtain the system's reliability before and after failure. Subsequently, we used an exponential recovery model to quantify the system's performance curve during the recovery phase, and finally utilized the resilient triangle area method to quantify its resilience size. Analyzing the CTCS3-300T train control on-board system, we found that the resilience of the system with cold standby redundancy design and hot standby redundancy design were 89.44 % and 87.34 %, respectively, indicating a slight decrease in system performance after recovery from failures compared to pre-failure levels. At that time, it was necessary to adjust operational plans based on actual conditions to avoid greater impact on the railway network. This paper realizes performance resilience of train control on-board system after failure recovery, which can be applied to similar systems and provide theoretical references for realizing intelligent maintenance of the high-speed train.

Unmasking and Quantifying Racial Bias of Large Language Models in Medical Report Generation.

Large language models like GPT-3.5-turbo and GPT-4 hold promise for healthcare professionals, but they may inadvertently inherit biases during their training, potentially affecting their utility in medical applications. Despite few attempts in the past, the precise impact and extent of these biases remain uncertain. Through both qualitative and quantitative analyses, we find that these models tend to project higher costs and longer hospitalizations for White populations and exhibit optimistic views in challenging medical scenarios with much higher survival rates. These biases, which mirror real-world healthcare disparities, are evident in the generation of patient backgrounds, the association of specific diseases with certain races, and disparities in treatment recommendations, etc. Our findings underscore the critical need for future research to address and mitigate biases in language models, especially in critical healthcare applications, to ensure fair and accurate outcomes for all patients.

Predictors of self-regulation fatigue patterns in patients before total knee arthroplasty: A cross-sectional study.

BACKGROUND: Patients with total knee arthroplasty encounter stressful events that consume their coping resources, resulting in self-control fatigue. Few studies have focused on this phenomenon. AIM: To identify subgroups of patients before total knee arthroplasty according to the heterogeneous patterns of self-regulation fatigue and to analyse the predictors of subtypes. METHODS: A total of 210 patients awaiting total knee arthroplasty were enrolled. Data of demographic characteristics, clinical characteristics, psychological and social factors were collected. Latent profile analysis was employed to define the subgroups. Predictors of patterns were identified using multinomial logistic regression. RESULTS: Three latent classes were identified: the low, medium, and high self-regulation fatigue classes. For the high self-regulation fatigue class, lower levels of hope, social support, self-efficacy and education were major predictors. CONCLUSION: These predictors of patients with different levels of self-regulation fatigue provide evidence for the identification of vulnerable populations and lay a foundation for targeted interventions.

[Life Cycle Carbon Emission Accounting and Emission Reduction Potential Assessment of Steel Industry].

The steel industry is one of the most carbon-intensive industries in China. To analyze the carbon emission and carbon reduction potential of the steel industry in the life cycle, a carbon emission accounting model was built from the perspective of the life cycle. Taking the year 2020 as an example, an empirical analysis was carried out to predict and evaluate the carbon reduction potential of the steel industry in the life cycle by optimizing four variables, namely, scrap usage, fossil fuel combustion, electric power carbon footprint factor, and clean transportation proportion. At the same time, sensitivity analysis was used to determine the key degree of factors affecting carbon emission reduction in the life cycle of steel. The results showed that in 2020, the total life cycle CO2 emissions of the steel industry in China was approximately 2.404 billion tons, of which the acquisition and processing of raw materials were the key links in the carbon emissions of the steel industry, accounting for more than 98% of the total life cycle CO2 emissions of the steel industry. From the analysis of CO2 emission source categories, fossil fuel savings and outsourcing power cleaning were the top priorities of carbon reduction in the steel industry. By 2025, the steel industry could achieve 20%, 6%, 5%, and 1% carbon emission reduction potential by respectively promoting low-carbon technology, optimizing the power structure, increasing the number of steel scraps, and increasing the proportion of clean transportation. The fossil fuel combustion had the most significant impact on the life cycle CO2 emissions of the steel industry, followed by the electric power carbon footprint factor and scrap steelmaking usage. With regard to low-carbon technologies in the steel industry, in the short term, the promotion of low-carbon technologies in the steel rolling process and blast furnace ironmaking process should be the main focus. Later, with the gradual increase in the proportion of electric furnace steelmaking, the promotion of low-carbon technologies in the electric furnace steelmaking process will significantly improve the carbon emission reduction potential of the steel industry throughout its life cycle.

Blinding assessment in clinical trials of traditional Chinese medicine: Exploratory principles and protocol.

As one of the key components of clinical trials, blinding, if successfully implemented, can help to mitigate the risks of implementation bias and measurement bias, consequently improving the validity and reliability of the trial results. However, successful blinding in clinical trials of traditional Chinese medicine (TCM) is hard to achieve, and the evaluation of blinding success through blinding assessment lacks established guidelines. Taking into account the challenges associated with blinding in the TCM field, here we present a framework for assessing blinding. Further, this study proposes a blinding assessment protocol for TCM clinical trials, building upon the framework and the existing methods. An assessment report checklist and an approach for evaluating the assessment results are presented based on the proposed protocol. It is anticipated that these improvements to blinding assessment will generate greater awareness among researchers, facilitate the standardization of blinding, and augment the blinding effectiveness. The use of this blinding assessment may further advance the quality and precision of TCM clinical trials and improve the accuracy of the trial results. The blinding assessment protocol will undergo continued optimization and refinement, drawing upon expert consensus and experience derived from clinical trials. Please cite this article as: Wang XC, Liu XY, Shi KL, Meng QG, Yu YF, Wang SY, Wang J, Qu C, Lei C, Yu XP. Blinding assessment in clinical trials of traditional Chinese medicine: Exploratory principles and protocol. J Integr Med. 2023; 21(6): 528-536.

Leveraging opportunity of low carbon transition by super-emitter cities in China.

Chinese cities are core in the national carbon mitigation and largely affect global decarbonisation initiatives, yet disparities between cities challenge country-wide progress. Low-carbon transition should preferably lead to a convergence of both equity and mitigation targets among cities. Inter-city supply chains that link the production and consumption of cities are a factor in shaping inequality and mitigation but less considered aggregately. Here, we modelled supply chains of 309 Chinese cities for 2012 to quantify carbon footprint inequality, as well as explored a leverage opportunity to achieve an inclusive low-carbon transition. We revealed significant carbon inequalities: the 10 richest cities in China have per capita carbon footprints comparable to the US level, while half of the Chinese cities sit below the global average. Inter-city supply chains in China, which are associated with 80% of carbon emissions, imply substantial carbon leakage risks and also contribute to socioeconomic disparities. However, the significant carbon inequality implies a leveraging opportunity that substantial mitigation can be achieved by 32 super-emitting cities. If the super-emitting cities adopt their differentiated mitigation pathway based on affluence, industrial structure, and role of supply chains, up to 1.4 Gt carbon quota can be created, raising 30% of the projected carbon quota to carbon peak. The additional carbon quota allows the average living standard of the other 60% of Chinese people to reach an upper-middle-income level, highlighting collaborative mechanism at the city level has a great potential to lead to a convergence of both equity and mitigation targets.

Machine-Aided Bridge Deck Crack Condition State Assessment Using Artificial Intelligence.

The Federal Highway Administration (FHWA) mandates biannual bridge inspections to assess the condition of all bridges in the United States. These inspections are recorded in the National Bridge Inventory (NBI) and the respective state's databases to manage, study, and analyze the data. As FHWA specifications become more complex, inspections require more training and field time. Recently, element-level inspections were added, assigning a condition state to each minor element in the bridge. To address this new requirement, a machine-aided bridge inspection method was developed using artificial intelligence (AI) to assist inspectors. The proposed method focuses on the condition state assessment of cracking in reinforced concrete bridge deck elements. The deep learning-based workflow integrated with image classification and semantic segmentation methods is utilized to extract information from images and evaluate the condition state of cracks according to FHWA specifications. The new workflow uses a deep neural network to extract information required by the bridge inspection manual, enabling the determination of the condition state of cracks in the deck. The results of experimentation demonstrate the effectiveness of this workflow for this application. The method also balances the costs and risks associated with increasing levels of AI involvement, enabling inspectors to better manage their resources. This AI-based method can be implemented by asset owners, such as Departments of Transportation, to better serve communities.

Health State Assessment of Industrial Equipment Driven by the Fusion of Digital Twin Model and Intelligent Algorithm.

Equipment health state assessment is of great significance to improve the efficiency of industrial equipment maintenance support and realize accurate support. Using the method driven by the fusion of digital twin model and intelligent algorithm can make the equipment health state assessment more suitable for the "accuracy" requirement of equipment support. Taking the neural network algorithm as an example, this paper studies the method of unit level health state assessment of equipment driven by the fusion of digital twin model and intelligent algorithm. The principle and opportunity of equipment health state assessment based on digital twin model are analyzed, the equipment health state grade is redefined from the data-driven perspective, the selection principles of assessment parameters are established, and the unit level health state assessment model of equipment based on digital twin model and neural network algorithm is established. The proposed method is implemented by programming with Python, and the effectiveness of the method is verified by a case study. It provides support for further research of equipment-level health state assessment and the decision-making of equipment maintenance and provides reference for the study of the combination of digital twin model and other intelligent algorithms for health state assessment.

Life cycle assessment of bamboo products: Review and harmonization.

Bamboo is considered a promising solution to mitigate climate change because of its carbon sequestration capability and versatile applications. Life cycle assessment (LCA) has been used to evaluate the environmental performance of various bamboo products. This study compared the Global Warming Potential (GWP) values of bamboo products with those of the corresponding benchmark materials (e.g., steel, concrete, plastics) through a comprehensive literature review of relevant LCA studies. The results showed that bamboo products often lead to lower GWP values. In several other cases, we also observed significant variability in the comparison results due to a wide range of assumptions regarding bamboo cultivation, processing, product manufacturing, energy supply, and choices of the LCA database adopted by the reviewed studies. We analyzed the key modeling assumptions for each life cycle stage of bamboo products and established a harmonized inventory dataset to reduce the uncertainty in modeling the processed bamboo (as a raw material for subsequently manufacturing various products). Based on the harmonized dataset, we conducted a cradle-to-gate LCA and concluded that the major contributor to the overall GWP result was electricity consumption (and associated carbon intensity of energy generation) during bamboo processing. We also concluded that future research was needed to improve the transparency, consistency, and comprehensiveness of LCA studies on bamboo products.

Inadvertently Generated PCBs in Consumer Products: Concentrations, Fate and Transport, and Preliminary Exposure Assessment.

Although commercial polychlorinated biphenyl (PCB) production was banned in 1979 under the Toxics Substance Control Act, inadvertent generation of PCBs through a variety of chemical production processes continues to contaminate products and waste streams. In this research, a total of 39 consumer products purchased from local and online retailer stores were analyzed for 209 PCB congeners. Inadvertent PCBs (iPCBs) were detected from seven products, and PCB-11 was the only congener detected in most of the samples, with a maximum concentration exceeding 800 ng/g. Emission of PCB-11 to air was studied from one craft foam sheet product using dynamic microchambers at 40 °C for about 120 days. PCB-11 migration from the product to house dust was also investigated. The IAQX program was then employed to estimate the emissions of PCB-11 from 10 craft foam sheets to indoor air in a 30 m3 room at 0.5 h-1 air change rate for 30 days. The predicted maximum PCB-11 concentration in the room air (156.8 ng/m3) and the measured concentration in dust (20 ng/g) were applied for the preliminary exposure assessment. The generated data from multipathway investigation in this work should be informative for further risk assessment and management for iPCBs.

Is the Discount Really Favorable? The Effect of Numeracy on Price Magnitude Judgment: Evidence From Electroencephalography.

Attractive price promotion will induce an unreasonable willingness to purchase, especially through shopping. However, it is not clear how numeracy, one of the essential abilities for understanding and applying numbers, influences the process of purchase judgment. In total, 61 participants were recruited to perform a price promotion task using electroencephalography. The results showed that consumers with low numeracy performed worse than their peers with high numeracy at the behavioral level, and they also had lower P3b amplitude and less alpha desynchronization, regardless of price promotion frameworks. These findings provided evidence on the processing of price information and provided further insights into how numeracy impacts price magnitude judgment.

Secondary metabolite pathway of SDG (secoisolariciresinol) was observed to trigger ROS scavenging system in response to Ca2+ stress in cotton.

Ca2+ is an essential nutrient for plants and animals which plays an important role in plant signal transduction. Although the function and regulation of mechanism of Ca2+ in alleviating various biotic and abiotic stresses in plants have been studied deeply, the molecular mechanism to adapt high Ca2+ stress is still unclear in cotton. In this study, 103 cotton accessions were germinated under 200 mM CaCl2 stress, and two extremely Ca2+-resistant (Zhong 9807, R) and Ca2+-sensitive (CRI 50, S) genotypes were selected from 103 cotton accessions. The two accessions were then germinated for 5 days in 0 mM CaCl2 and 200 mM CaCl2 respectively, after which they were sampled for transcriptome sequencing. Morphological and physiological analyses suggested that PLR2 specifically expressed in R may enhance the ability of cotton to scavenge ROS by promoting the synthesis of SDG. In conclusion, this study proposed the adaptation mechanisms to response to the high Ca2+ stress in cotton which can contribute to improve the stress resistance of cotton.

Exploring the characteristics and driving factors of coupling coordination of regional sustainable development: evidence from China's 31 provinces.

China's provinces' development conditions, levels, and models are quite different. Indeed, the contradictions in development are becoming increasingly prominent, and the task of sustainable development is becoming increasingly arduous. The difference in the coordination degree of the development of the economy, sci-technology, ecology, resources, and society (ESERS), and the influencing factors among the research areas have become the most concerned scientific proposition of regional sustainable development. This paper measures the ESERS coupling and coordinated development relationship among the five development levels of China's provinces on the coupling coordination model. The spatial autoregressive (SAR) and geographically weighted regression (GWR) models are used to capture the spatial correlation and spatial heterogeneity of the sustainable and coordinated development of various districts in China. The main research conclusions are as follows: (1) the coupling coordination relationship of different regions in China remained at the mild and moderate maladjusted recession stage. In each of the five dimensions (ESERS), the coupling coordination relationship is relatively weak. (2) In terms of temporal distribution, among the four geographical regions of China, except the northeast, the development of ESERS in the eastern, central, and western regions is shifting to a coordinated balance. In terms of spatial distribution, the unbalanced development of ESERS is mainly concentrated in the northeast and part of the western regions. (3) The backward industrial structure is the main reason leading to the unbalanced development of ESERS in China, while the degree of opening up, eco-environmental governance ability, and education investment intensity are the critical factors leading to the development differentiation of ESERS in different regions of China. Overall optimization of industrial structure is the best way to improve the balance of the overall development of ESERS in China, and strengthening human ecology and international construction is the more effective way to narrow the development differences of regional ESERS.

Assessment of the effectiveness of BOPPPS-based hybrid teaching model in physiology education.

BACKGROUND: Online teaching has become increasingly common in higher education of the post-pandemic era. While a traditional face-to-face lecture or offline teaching remains very important and necessary for students to learn the medical knowledge systematically, guided by the BOPPPS teaching model, combination of online and offline learning approaches has become an unavoidable trend for maximizing teaching efficiency. However, in physiological education, the effectiveness of combined online teaching and offline teaching models remains poorly assessed. The present study aims at providing an assessment to the hybrid teaching model. METHODS: The study was performed among undergraduate medical students of Class 2017 ~ 2019 in the Physiology course in Harbin Medical University during 2018-2020. Based on established offline teaching model with BOPPPS components in 2018, we incorporated online teaching contents into it to form a hybrid BOPPPS teaching model (HBOPPPS, in brief), preliminarily in 2019 and completely in 2020. HBOPPPS effectiveness was assessed through comparing the final examination scores of both objective (multi-choice and single answer questions) and subjective (short and long essays) questions between classes taught with different modalities. RESULTS: The final examination score of students in Class 2019 (83.9 ± 0.5) who were taught with the HBOPPPS was significantly higher than that in Class 2017 (81.1 ± 0.6) taught with offline BOPPPS and in Class 2018 (82.0 ± 0.5) taught with immature HBOPPPS. The difference mainly attributed to the increase in average subjective scores (41.6 ± 0.3 in Class 2019, 41.4 ± 0.3 in Class 2018, and 38.2 ± 0.4 in Class 2017). In the questionnaire about the HBOPPPS among students in Class 2019, 86.2% responded positively and 79.4% perceived improvement in their learning ability. In addition, 73.5% of the students appreciated the reproducibility of learning content and 54.2% valued the flexibility of HBOPPPS. Lastly, 61.7% of the students preferred the HBOPPPS relative to BOPPPS in future learning. CONCLUSIONS: HBOPPPS is likely a more effective teaching model and useful for enhancing effectiveness of Physiology teaching. This is attributable to the reproducibility and flexibility as well as the increased learning initiatives.

Delayed extubation in spine surgery is associated with increased postoperative complications and hospital episode-based resource utilization.

STUDY OBJECTIVE: To elucidate the association between delayed extubation, postoperative complications, and episode-based resource utilization. DESIGN: Retrospective Propensity-Matched Cohort Study. SETTING: Single Large Academic Medical Center. PATIENTS: The computerized anesthetic records of 17,223 patients undergoing spine surgery from January 2006 through November 2016 were reviewed for this study. The records of 11,421 patients met inclusion criteria for final analysis, with 527 subjects who had delayed extubation following their procedure. INTERVENTIONS: Delayed extubation, defined as patients not extubated prior to leaving the operating room. MEASUREMENTS: Computerized anesthetic records of spine surgery patients were analyzed retrospectively. Corresponding Medicare Severity Diagnosis Related Group numbers (MS-DRGs) were then identified, as well as associated lengths of stay and costs of care. We compared hospital-acquired International Classification of Diseases-9 (ICD-9) and ICD-10 postoperative complication codes linked to each record to assess differences in outcome. MAIN RESULTS: Increasing medical and surgical complexity is associated with delayed extubation. Using propensity score matching, delayed extubation was independently associated with a higher likelihood of any postoperative complication (Odds Ratio [OR]: 1.79; 95% Confidence Interval [CI]: 1.23-2.61); major complications (OR: 2.22; 95% CI: 1.31-3.76); prolonged length of hospital stay (Hazard Ratio [HR]: 0.82 (0.72, 0.95), p = 0.006); prolonged Intensive Care Unit (ICU) stay (HR: 0.68 (0.61, 0.76), p < 0.001); and were less likely to be discharged home (OR: 1.40 (1.02, 1.92), p = 0.036). Propensity score matching demonstrated that anesthesiologist handoff was not independently associated with any of the examined adverse outcomes. CONCLUSIONS: Delayed extubation after spine surgery was associated with a statistically significant increased incidence of postoperative complications as well as increased hospital episode-based resource utilization in the form of increased hospital length of stay, ICU length of stay, post-acute care at a facility, and higher cost of hospitalization. Although anesthesiologist handoff was associated with delayed extubation, it was not independently associated with postoperative complications when propensity score matching was applied.

Increasing disparities in the embedded carbon emissions of provincial urban households in China.

In the context of carbon neutrality and the National Economic Circle Strategy, understanding regional disparities in carbon emissions from household consumption is conducive to regional coordination as well as high-quality and low-carbon development in China. In this study, a multiregional input-output (MRIO) model and structural decomposition analysis (SDA) are adopted to investigate the regional disparity change trends of embedded carbon emissions (ECEs) from urban households and the underlying drivers during the rapid economic development period from 2002 to 2012 in China. The results indicate that the eastern regions tended to have larger increments in total urban household ECEs, while the western regions tended to have faster growth rates. An increasing disparity and evident outsourcing pattern can be observed during the study period. The consumption level had a strong positive effect on urban household ECEs in all of the provinces, while the carbon efficiency, consumption pattern, production structure, and population size had differentiated offsetting effects on urban household ECEs in various provinces. The results obtained in this study are conducive to promoting joint efforts for carbon emission reduction and narrowing regional disparities.

Methylated SFRP2 and SDC2 in stool specimens for Colorectal Cancer early detection: A cost-effective strategy for Chinese population.

Background: The aim of this study was to evaluate the feasibility of combination of methylated SFRP2 and methylated SDC2 (SpecColon test) in stool specimens for colorectal cancer (CRC) early detection and to optimize the cut-off value of methylated SFRP2 and methylated SDC2. Methods: Approximately 5 g of stool specimen each was collected from 420 subjects (291 in the training cohort and 129 in the validation cohort). Stool DNA was extracted and bisulfite-converted, followed by detection of methylated level of SFRP2 and SDC2. Youden index was employed to determine the cut-off value. Results: The whole operating time for stool SpecColon test takes less than 5 hours. The limit of detection of combination of methylated SFRP2 and methylated SDC2 was as low as 5 pg per reaction. The optimized cut-off value was methylated SFRP2 analyzed by 3/3 rule and methylated SDC2 analyzed by 2/3 rule. In the training cohort, the sensitivities of stool SpecColon test for detecting AA and early stage CRC (stage 0-II) were 53.8% (95% CI: 26.1%-79.6%) and 89.1% (95% CI: 77.1%-95.5%) with a specificity of 93.5% (95% CI: 87.2%-96.9%), and the AUC for CRC diagnosis was 0.879 (95% CI: 0.830-0.928). Similar performance was achieved by SpecColon test also in the validation cohort, where its sensitivities for detecting AA and early stage CRC (stage 0-II) were 61.5% (95% CI: 32.3-84.9%) and 88.5% (95% CI: 68.5%-97.0%) with a specificity of 89.5% (95% CI: 74.3-96.7%). Conclusion: Combined detections of methylated SFRP2 and methylated SDC2 in stool samples demonstrated high sensitivities and specificity for the detection of AA and early stage CRC. Therefore, this combination has the potential to become an accurate and cost-effective tool for CRC early detection.