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This study uses logistic and Poisson regression models to examine the factors influencing the adoption of sustainable land management (SLM) practices in Mali using two rounds of the nationally representative survey Enquête Agricole de Conjoncture Intégrée aux Conditions de Vie des Ménages. The SLMs considered include the application of organic fertilizers, the application of inorganic fertilizers, the use of improved seeds, and the practice of intercropping. On average the application of organic fertilizers (39.2%), and inorganic fertilizers (28.7%) are the most frequent SLM practices among Malian farmers, and between 2014 and 2017, we observe a decline in the practice of intercropping. The regression results show that farmers’ adoption of different SLMs is significantly associated with biophysical factors (average temperature, climate type, plot size, plot shape, and location), demographic factors (age, gender, education, household size), and socioeconomic factors (number of cultivated plots, livelihood diversification, type of crop grown, market access, credit access, economic shocks, and social capital). Our findings suggest that policymakers and agricultural development agencies in Mali need to adopt a multidimensional policy framework to unlock the untapped potential of SLM practices in promoting sustainable agriculture and food security.

AbstractLogistic regression models have a severe problem called separation. The maximum likelihood estimator does not exist in logistic regression models for data structures under separation. Under separation, the forcibly estimated maximum likelihood estimate may have an extremely large value. Separation often occurs when the size of dataset is small. Consequently, goodness-of-fit measures based on the likelihood ratio and those based on covariance functions using the maximum likelihood estimate indicate that the model is excessively good regardless of the cause of the separation. The Firth and exact logistic regression methods are valid estimation methods for separation problems. Therefore, we propose methods to reasonably evaluate the goodness-of-fit measures of statistical models under separation with dataset of a small sample size with the abovementioned methods. The goodness-of-fit measures based on covariance functions which are a generalization of the multiple correlation coefficient, referred to as the regression correlation coefficient and the entropy coefficient of determination are then used combined with the abovementioned methods for the separation data. In addition, we conducted a data analysis using the definition of the non separation ratio based on the regression depth. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) hold promise for curbing emissions and promoting sustainable urban development in India, aligning with Sustainable Development Goals (SDGs). However, their uptake remains below expectations. This study delves into factors influencing EV adoption, exploring socioeconomic determinants, technical specifications, cost considerations, and policy impacts. It also assesses the role of charging infrastructure accessibility, demographic disparities, and battery end‐of‐life perceptions. The research identifies key socio‐economic factors driving consumer choices and perceptions of EVs through a comprehensive survey and logistic regression analysis. The findings will guide policymakers and businesses in developing effective strategies to promote EV use, supporting SDGs related to sustainable cities, communities, and economic growth. The insights gained from this study can inspire private sector growth and innovation, leading to new business opportunities that benefit companies and consumers. This research represents a crucial milestone in advancing sustainable transportation solutions in India and creating a better future. [ABSTRACT FROM AUTHOR]

Locusts are globally recognized as major pest threats. In the first half of the 20th century, the South American locust caused great economic losses. After the implementation of preventive management, large‐scale upsurges ceased. In 2015, resurgence of S. cancellata led to swarms affecting northern Argentina, Paraguay, and Bolivia, prompting control agencies to address an almost forgotten problem. After six decades without a major locust outbreak, there were limited and outdated studies on this species. This study aims to identify key environmental factors associated with the spatial distribution of S. cancellata oviposition sites. We focus on explanatory variables that represent physical and chemical properties of soil and vegetation cover. To understand the relationships between each potential explanatory variable and the presence‐absence of S. cancellata oviposition sites, we first performed regression analyses applying a linear and quadratic structure for each explanatory variable. Then, we performed comparisons of logistic regression models in a multi‐model inference framework, where CAIC and weights of evidence were analysed. Our results show that the South American locusts chose to lay their eggs in areas with a low proportion of natural forest and flooded grasslands and a high proportion of non‐vegetated areas, where the soils are flat, with neutral pH, and low salinity. We also determined that an increase in the proportion of cultivated areas is associated with an increase in the probability of breeding presence of this species. The locust's habitat falls within the Dry Chaco, a global deforestation hotspot, evidencing a rapid replacing of forests for plantations. Since both the diminish of forest and the increase in cultivated areas are associated with an increase in oviposition sites, we consider that breeding areas will likely increase. The results found herein can be used to map the potential breeding habitats to help preventive management against the South American locust. [ABSTRACT FROM AUTHOR]

Landslide susceptibility denotes the likelihood of a disaster event under specific conditions. The assessment of landslide susceptibility has transitioned from qualitative to quantitative methods. With the integration of information technology in geological hazard analysis, a range of quantitative models for assessing landslide susceptibility has emerged and is now widely used. To compare and evaluate the accuracy of these models, this study focuses on Xupu County in Hunan Province, applying several models, including the CF model, FR model, CF-LR coupled model, FR-LR coupled model, SVM model, and RF model, to assess regional landslide susceptibility. ROC curves are used to evaluate the reliability of the model's predictions. The evaluation results reveal that the CF model (AUC = 0.756), FR model (AUC = 0.764), CF-LR model (AUC = 0.776), FR-LR model (AUC = 0.781), SVM model (AUC = 0.814), and RF model (AUC = 0.912) all have AUC values within the range of 0.7–0.9, indicating that the overall accuracy of the models is good and can provide a reference for landslide susceptibility zoning in the study area. Among these, the Random Forest model demonstrates the best accuracy for landslide susceptibility zoning in the study area. By extracting the extremely high susceptibility zones from the landslide susceptibility zonings obtained by six models, a comparative analysis of model adaptability was conducted. The results indicate that the Random Forest model has the best adaptability under specific conditions in Xupu County. [ABSTRACT FROM AUTHOR]

Aims: Patients with proximal femoral fractures (PFFs) are often multimorbid, thus unplanned readmissions following surgery are common. We therefore aimed to analyze 30-day and one-year readmission rates, reasons for, and factors associated with, readmission risk in a cohort of patients with surgically treated PFFs across Austria. Methods: Data from 11,270 patients with PFFs, treated surgically (osteosyntheses, n = 6,435; endoprostheses, n = 4,835) at Austrian hospitals within a one-year period (January to December 2021) was retrieved from the Leistungsorientierte Krankenanstaltenfinanzierung (Achievement-Oriented Hospital Financing). The 30-day and one-year readmission rates were reported. Readmission risk for any complication, as well as general medicine-, internal medicine-, and surgery/injury-associated complications, and factors associated with readmissions, were investigated. Results: The 30-day and one-year readmission rates due to any complication were 15% and 47%, respectively. The 30-day readmission rate (p = 0.001) was higher in endoprosthesis than osteosynthesis patients; this was not the case for the one-year readmission rate (p = 0.138). Internal medicine- (n = 2,273 (20%)) and surgery/injury-associated complications (n = 1,612 (14%)) were the most common reason for one-year readmission. Regardless of the surgical procedure, male sex was significantly associated with higher readmission risk due to any, as well as internal medicine-associated, complication. Advanced age was significantly associated with higher readmission risk after osteosynthesis. In both cohorts, treatment at mid-sized hospitals was significantly associated with lower readmission risk due to any complication, while prolonged length of stay was associated with higher one-year readmission risks due to any complication, as well as internal-medicine associated complications. Conclusion: Future health policy decisions in Austria should focus on optimization of perioperative and post-discharge management of this vulnerable patient population. Cite this article: Bone Jt Open 2024;5(4):294–303.

Objective. To determine incidence rate and risk factors for the development of venous thromboembolism in complicated cervical spine injury. Material and Methods. The study included 34 patients with acute complicated cervical spine injury. Inclusion criteria were newly diagnosed venous thromboembolic complications, and application of low-frequency piezothromboelastography to study the hemostasis system. All patients received standard drug thromboprophylaxis. Patients were divided into two study groups: Group I included 21 patients with venous thromboembolic complications, and Group II – 13 patients without thromboembolic complications. Results. The incidence of venous thromboembolism in the total sample was 61.8 %. Pulmonary artery embolism developed in 4.7 % of cases. In 91,0 % of cases, thrombosis was asymptomatic. The state of the hemostatic system in Group I before the start of thromboprophylaxis was characterized by chronometric hypocoagulation, and structural hypercoagulation with a 2.6-fold increase in the intensity of clot retraction and lysis. In Group II, there was chronometric and structural hypercoagulation with a 14.4-fold increase in the intensity of clot retraction and lysis. The main significant predictors of the development of venous thromboembolism were identified as intestinal paresis (p = 0.004), absence of changes in neurological status (p = 0.012), length of stay in the ICU (p = 0.025), and length of hospital stay (p = 0.039). The building of a multivariate logistic regression model revealed multiplicative significant predictors of the development of thromboembolism. It has been shown that the presence of intestinal paresis is associated with a 25.07-fold increase in the chances of developing DVT of lower extremities. Conclusion. Considering the high incidence of venous thromboembolic complications in patients with complicated cervical spine injury, further research is required to study the effectiveness and safety of correction of drug thromboprophylaxis regimens in the form of increasing doses of anticoagulants or the frequency of their administration.

Aims: It is unclear whether mortality outcomes differ for patients undergoing total hip arthroplasty (THA) or total knee arthroplasty (TKA) surgery who are readmitted to the index hospital where their surgery was performed, or to another hospital. Methods: We analyzed linked hospital and death records for residents of New South Wales, Australia, aged ≥ 18 years who had an emergency readmission within 90 days following THA or TKA surgery between 2003 and 2022. Multivariable modelling was used to identify factors associated with non-index readmission and to evaluate associations of readmission destination (non-index vs index) with 90-day and one-year mortality. Results: Of 394,248 joint arthroplasty patients (THA = 149,456; TKA = 244,792), 9.5% (n = 37,431) were readmitted within 90 days, and 53.7% of these were admitted to a non-index hospital. Non-index readmission was more prevalent among patients who underwent surgery in private hospitals (60%). Patients who were readmitted for non-orthopaedic conditions (62.8%), were more likely to return to a non-index hospital compared to those readmitted for orthopaedic complications (39.5%). Factors associated with non-index readmission included older age, higher socioeconomic status, private health insurance, and residence in a rural or remote area. Non-index readmission was significantly associated with 90 day (adjusted odds ratio (aOR) 1.69; 95% confidence interval (CI) 1.39 to 2.05) and one-year mortality (aOR 1.31; 95% CI 1.16 to 1.47). Associations between non-index readmission and mortality were similar for patients readmitted with orthopaedic and non-orthopaedic complications (90-day mortality aOR 1.61; 95% CI 0.98 to 2.64, and aOR 1.67; 95% CI 1.35 to 2.06, respectively). Conclusion: Non-index readmission was associated with increased mortality, irrespective of whether the readmission was for orthopaedic complications or other conditions. Cite this article: Bone Jt Open 2024;5(1):60–68.

Aims: In-hospital length of stay (LOS) and discharge dispositions following arthroplasty could act as surrogate measures for improvement in patient pathways, and have major cost saving implications for healthcare providers. With the ever-growing adoption of robotic technology in arthroplasty, it is imperative to evaluate its impact on LOS. The objectives of this study were to compare LOS and discharge dispositions following robotic arm-assisted total knee arthroplasty (RO TKA) and unicompartmental arthroplasty (RO UKA) versus conventional technique (CO TKA and UKA). Methods: This large-scale, single-institution study included patients of any age undergoing primary TKA (n = 1,375) or UKA (n = 337) for any cause between May 2019 and January 2023. Data extracted included patient demographics, LOS, need for post anaesthesia care unit (PACU) admission, anaesthesia type, readmission within 30 days, and discharge dispositions. Univariate and multivariate logistic regression models were also employed to identify factors and patient characteristics related to delayed discharge. Results: The median LOS in the RO TKA group was 76 hours (interquartile range (IQR) 54 to 104) versus 82.5 (IQR 58 to 127) in the CO TKA group (p < 0.001) and 54 hours (IQR 34 to 77) in the RO UKA versus 58 (IQR 35 to 81) in the CO UKA (p = 0.031). Discharge dispositions were comparable between the two groups. A higher percentage of patients undergoing CO TKA required PACU admission (8% vs 5.2%; p = 0.040). Conclusion: Our study showed that robotic arm assistance was associated with a shorter LOS in patients undergoing primary UKA and TKA, and no difference in the discharge destinations. Our results suggest that robotic arm assistance could be advantageous in partly addressing the upsurge of knee arthroplasty procedures and the concomitant healthcare burden; however, this needs to be corroborated by long-term cost-effectiveness analyses and data from randomized controlled studies. Cite this article: Bone Jt Open 2023;4(10):791–800.

This study focuses on investigating the use of mobile phones among young drivers by employing an online questionnaire survey data. Ordinal logistic regression model was used for modelling the probabilities of crashes due to different uses of mobile phone while driving. Moreover, binary logistic regression models were used for predicting the probabilities of different uses of mobile phone. Logistic regression models revealed that texting and internet use have the same likelihood of causing crashes. Drivers having prior experience of being fined for mobile phone use, also showed a higher tendency to be involved in 2 crashes. Moreover, these drivers had a higher likelihood of being involved in texting, as compared to other uses of mobile phones. Drivers with more education had a higher tendency for internet use during driving. Drivers who use mobile phone for long periods during driving have a lesser tendency to get involved in texting, internet use or GPS navigation. Moreover, drivers with a previous crash record have less likelihood of being involved in texting. The models of this study can be useful in developing effective road safety measures. Clustering was also applied in this study which reinforced the findings of the statistical analysis and models. [ABSTRACT FROM AUTHOR]

In this study, a tandem clustering is applied on data collected by an Italian public transport company. Three clusters of evader passengers are discovered. Next, for each cluster, the influence of significant determinants in evaluating the chance of being a frequent fare evader is shown by logistic regression models. Members of Cluster 1 are a small segment of choice-workers, who seldom evade fares significantly. Members of Cluster 2 represent a big segment of captive students, who often evade the fare. Members of Cluster 3 are a medium segment of captive unemployed, who always evade the fare. The logistic regression models show that attributes related to the situational factors are significant, and honesty is the common variable that significantly affects the chance of being a frequent fare evader among segments. These outcomes are relevant and useful for both research and practice. Indeed, this paper contributes to the empirical understanding of the determinants of being a frequent fare evader for segments a posteriori selected. Moreover, it helps PTCs to better understand how some segments differ from each other. [ABSTRACT FROM AUTHOR]

Research background: The sustainability reflected by the CSR of luxury fashion businesses, should meet stakeholders´ expectations and lead to an increase in customers´ buying decisions.

The modelling of urban drainage systems is an important aspect of their design process and long-term statistical modelling using historical rain series is commonly used. The objective of this study is to determine whether logistic regression models that use rainfall event statistics can be a viable alternative to create job lists with fewer extraneous events. Two methods are used to develop a regression model; both use iterative stepwise algorithms to select the rain variables to include and both perform similarly. The resulting model is able to capture ∼90% of the relevant events with ∼50% fewer jobs compared to the reference job list. The results suggest that there is no right threshold to use, but instead this methodology facilitates balancing the number of jobs with the desired level of precision of the results. In all cases, it is possible to greatly decrease the number of jobs that need to be run. The methodology works relatively well on different nodes in the system, though node characteristics appear to impact the amount of CSOs captured. HIGHLIGHTS Job lists for LTS simulations can be targeted using logistic regression models.; The logistic regression models draws on statistical data from the rainfall input data.; The number of jobs can be drastically reduced while mostly maintaining the relevant jobs.; The proposed methodology can be used to run more efficient LTS simulations.;

Background: Breast cancer neoadjuvant chemotherapy (NACT) allows for assessing tumor sensitivity to systemic treatment, planning adjuvant treatment and follow-up. However, a sufficiently large number of patients fail to achieve the desired level of pathological tumor response while optimal early response assessment methods have not been established now. In our study, we simultaneously assessed the early chemotherapy-induced changes in the tumor volume by ultrasound (US), the tumor oxygenation by diffuse optical spectroscopy imaging (DOSI), and the state of the tumor vascular bed by Doppler US to elaborate the predictive criteria of breast tumor response to treatment. Methods: A total of 133 patients with a confirmed diagnosis of invasive breast cancer stage II to III admitted to NACT following definitive breast surgery were enrolled, of those 103 were included in the final analysis. Tumor oxygenation by DOSI, tumor volume by US, and tumor vascularization by Doppler US were determined before the first and second cycle of NACT. After NACT completion, patients underwent surgery followed by pathological examination and assessment of the pathological tumor response. On the basis of these, data regression predictive models were created. Results: We observed changes in all three parameters 3 weeks after the start of the treatment. However, a high predictive potential for early assessment of tumor sensitivity to NACT demonstrated only the level of oxygenation, ΔStO2, (ρ = 0.802, p ≤ 0.01). The regression model predicts the tumor response with a high probability of a correct conclusion (89.3%). The "Tumor volume" model and the "Vascularization index" model did not accurately predict the absence of a pathological tumor response to treatment (60.9% and 58.7%, respectively), while predicting a positive response to treatment was relatively better (78.9% and 75.4%, respectively). Conclusions: Diffuse optical spectroscopy imaging appeared to be a robust tool for early predicting breast cancer response to chemotherapy. It may help identify patients who need additional molecular genetic study of the tumor in order to find the source of resistance to treatment, as well as to correct the treatment regimen. [ABSTRACT FROM AUTHOR]

Rice grain discoloration (RGD) is a disease of complex aetiology for which there are no resistant varieties. Due to the need to better define the environmental conditions that favour the disease, the aims of this work were to (i) identify the predominant fungi associated, (ii) determine the meteorological variables most closely related, and (iii) develop preliminary weather‐based models to predict binary levels of RGD incidence. After analysing 123 rice grain samples under natural infection conditions from rice‐cropping regions throughout Corrientes province, Argentina, we found that RGD was mainly associated with Alternaria padwickii (14.2%) and Microdochium albescens (13.7%). The strongest associations between weather variables and RGD incidence were observed in a susceptible critical period (Scp) that extended from the rice flowering stage until 870 accumulated degree days (Scp lasting 32 days, ±7 days). The binary response logistic model including the weather variables DPrecT (which combined the effect of the simultaneous daily occurrence of precipitation lower than 12 mm and air temperature between 13 and 28°C), and DDMnT (sum of the exceeding amounts of daily min temperature from 23°C), was the most appropriate, showing prediction accuracy (PA) values of 84.6%. The univariate model that included DPrecT presented a PA of 82.1%. The logistic regression techniques here used to develop weather‐based models to estimate the probabilities of occurrence of binary levels of RGD can not only help to clarify and quantify the environmental effect on the development of RGD but also be useful tools to be included in future management strategies. [ABSTRACT FROM AUTHOR]

Background and Purpose: Prediction of futile recanalization (FR), i.e. failure of long-term functional independence despite full reperfusion in mechanical thrombectomy (MT), is instrumental in patients undergoing endovascular therapy. Methods: Retrospective single-center analysis of patients treated for anterior circulation LVO ensuing successful MT (mTICI 2c–3) between January 2014 and April 2019. FR was defined as modified Rankin Scale (mRS) 90 days after stroke onset > 2 or mRS > pre-stroke mRS. Multivariable analysis was performed with variables available before treatment initiation regarding their association with FR. Performance of the regression model was then compared with a model including parameters available after MT. Results: Successful MT was experienced by 549/1146 patients in total. FR occurred in 262/549 (47.7%) patients. Independent predictors of FR were male sex, odds ratio (OR) with 95% confidence interval (CI) 1.98 (1.31–3.05, p 0.001), age (OR 1.05, CI 1.03–1.07, p < 0.001), NIHSS on admission (OR 1.10, CI 1.06–1.13, p < 0.001), pre-stroke mRS (OR 1.22, CI 1.03–1.46, p 0.025), neutrophile-lymphocyte ratio (OR 1.03, CI 1.00–1.06, p 0.022), baseline ASPECTS (OR 0.77, CI 0.68–0.88, p < 0.001), and absence of bridging i.v. lysis (OR 1.62, 1.09–2.42, p 0.016). The prediction model's Area Under the Curve was 0.78 (CI 0.74–0.82) and increased with parameters available after MT to 0.86 (CI 0.83–0.89) with failure of early neurological improvement being the most important predictor of FR (OR 15.0, CI 7.2–33.8). Conclusion: A variety of preinterventional factors may predict FR with substantial certainty, but the prediction model can still be improved by considering parameters only available after MT, in particular early neurological improvement. [ABSTRACT FROM AUTHOR]

The notion of sentencehood in Mandarin Chinese is much less well-defined than in many other languages, with a block of clauses often joined by commas without conjunctions and with the period often occurring at the end of a block of clauses to indicate meaning completeness rather than the completeness of a sentential structure. The potential factors that may affect native Chinese speakers' judgment of meaning completeness and perception of sentence boundaries have not yet been systematically examined. In light of this research gap, this study investigates the factors that may play a role in native Chinese speakers' sentence boundary perception. To this end, we conducted text re-punctuation experiments in two separate groups, a training group and a testing group, using different stimuli texts. The stimuli texts were annotated with multiple levels of linguistic information to identify potentially relevant variables that could affect the participants' sentence boundary perception. Logistic regression and the Bayesian statistical methods were applied to test the potential effects of multiple variables on the participants' responses. The logistic regression model trained on the data from the training group achieved a high level of accuracy in predicting the responses by the testing group. The model revealed a more important role of semantic information than syntactic information in the participants' sentence boundary perception. The implications of our findings for understanding the perception of Chinese sentence boundaries are discussed. [ABSTRACT FROM AUTHOR]

Simple Summary: Despite the fact that sex estimation methods from crania are very popular in forensic anthropology, few validation studies have verified their accuracy and reliability in different populations. Different from craniometrics, for which validation studies have remarkably increased lately, the methods based on cranial morphology still need to be thoroughly investigated, even if a large consensus exists on the effects of population variability on sexual cranial dimorphism. When dealing with forensic contexts, appropriately-validated methods should be applied for building accurate biological profiles. Since the possible sexual dimorphism variation of cranial morphological traits needs to be evaluated properly in various populations, in this study, we analyzed the accuracy of existing regression models for predicting sex from cranial morphological traits in an Italian contemporary/modern population. In addition, we propose new logistic regression models that are more accurate and specific for our sample. The results also update the reference standards for populations of this geographical area and provide an additional important warning on sexual dimorphism to anthropologists working in forensic contexts. Although not without subjectivity, the cranial trait scoring method is an easy visual method routinely used by forensic anthropologists in sex estimation. The revision presented by Walker in 2008 has introduced predictive models with good accuracies in the original populations. However, such models may lead to unsatisfactory performances when applied to populations that are different from the original. Therefore, this study aimed to test the sex predictive equations reported by Walker on a contemporary Italian population (177 individuals) in order to evaluate the reliability of the method and to identify potential sexual dimorphic differences between American and Italian individuals. In order to provide new reference data to be used by forensic experts dealing with human remains of modern/contemporary individuals from this geographical area, we designed logistic regression models specific to our population, whose accuracy was evaluated on a validation sample from the same population. In particular, we fitted logistic regression models for all possible combinations of the five cranial morphological traits (i.e., nuchal crest, mastoid process, orbital margin, glabella, and mental eminence). This approach provided a comprehensive set of population-specific equations that can be used in forensic contexts where crania might be retrieved with severe taphonomic damages, thus limiting the application of the method only to a few morphological features. The results proved once again that the effects of secular changes and biogeographic ancestry on sexual dimorphism of cranial morphological traits are remarkable, as highlighted by the low accuracy (from 56% to 78%) of the six Walker's equations when applied to our female sample. Among our fitted models, the one including the glabella and mastoid process was the most accurate since these features are more sexually dimorphic in our population. Finally, our models proved to have high predictive performances in both training and validation samples, with accuracy percentages up to 91.7% for Italian females, which represents a significant success in minimizing the potential misclassifications in real forensic scenarios. [ABSTRACT FROM AUTHOR]

Taking Shandong Province as the research area, we explored the willingness of farmers to grow grain and the influencing factors. By constructing the evaluation system of their willingness with 6 levels and 15 indicators based on field investigation, and quantitatively analyzing the influence degree and impact assessment of factors through a logistic regression model, the regional differences in farmers' willingness were summarized, and the influencing factors were recognized. This study indicates that there were obvious regional differences in farmers' willingness, which were the highest in the western region, the second in the eastern region, and the lowest in the central region. Specifically, the willingness varies significantly among cities, among which Laiwu has the highest willingness (0.76), while Tai'an has the lowest (0.41). The level of urbanization in different regions and the main influencing factors are different, and the same factor has different degrees of influence on cities, leading to regional differences. In terms of urbanization level, the main influencing factors in areas with high urbanization levels are the proportion of grain income and grain expenditure. However, in areas with a low urbanization level, it is the farmers' planting attitude. From the perspective of influence mode, different factors have positive and negative differences in the willingness. Additionally, farmers' willingness is becoming more and more rational, and more consideration is given to economic benefits. Among the influencing factors, the land planting mode, the proportion of grain income, and the proportion of grain expenditure are the most important factors, and 82% (11) of the cities are affected by the above three factors. Finally, the corresponding incentive measures are proposed by the regional differences in the influencing factors in various cities. [ABSTRACT FROM AUTHOR]

Background: The aim of the study was to predict the probability of intensive care unit (ICU) care for inpatient COVID-19 cases using clinical and artificial intelligence segmentation-based volumetric and CT-radiomics parameters on admission. Methods: Twenty-eight clinical/laboratory features, 21 volumetric parameters, and 74 radiomics parameters obtained by deep learning (DL)-based segmentations from CT examinations of 191 severe COVID-19 inpatients admitted between March 2020 and March 2021 were collected. Patients were divided into Group 1 (117 patients discharged from the inpatient service) and Group 2 (74 patients transferred to the ICU), and the differences between the groups were evaluated with the T-test and Mann–Whitney test. The sensitivities and specificities of significantly different parameters were evaluated by ROC analysis. Subsequently, 152 (79.5%) patients were assigned to the training/cross-validation set, and 39 (20.5%) patients were assigned to the test set. Clinical, radiological, and combined logit-fit models were generated by using the Bayesian information criterion from the training set and optimized via tenfold cross-validation. To simultaneously use all of the clinical, volumetric, and radiomics parameters, a random forest model was produced, and this model was trained by using a balanced training set created by adding synthetic data to the existing training/cross-validation set. The results of the models in predicting ICU patients were evaluated with the test set. Results: No parameter individually created a reliable classifier. When the test set was evaluated with the final models, the AUC values were 0.736, 0.708, and 0.794, the specificity values were 79.17%, 79.17%, and 87.50%, the sensitivity values were 66.67%, 60%, and 73.33%, and the F1 values were 0.67, 0.62, and 0.76 for the clinical, radiological, and combined logit-fit models, respectively. The random forest model that was trained with the balanced training/cross-validation set was the most successful model, achieving an AUC of 0.837, specificity of 87.50%, sensitivity of 80%, and F1 value of 0.80 in the test set. Conclusion: By using a machine learning algorithm that was composed of clinical and DL-segmentation-based radiological parameters and that was trained with a balanced data set, COVID-19 patients who may require intensive care could be successfully predicted. [ABSTRACT FROM AUTHOR]

Customer attrition is the phenomenon wherein a customer leaves a service provider. With the growing competition in the service sector, preventing customer attrition has become critical for sustainability, as it is well established that retaining existing customers is more profitable than acquiring new customers (Jacob, 1994). This gives customer attrition analytics the challenging task of predicting which customers are likely to leave, and of subsequently designing and implementing retention programmes for these customers. Customer analytics has made many strides in marketing, employer desirability, and branding, but has so far made limited strides in the recruitment industry space. The objective of the study is to identify the factors affecting a candidate's decision to accept a job opportunity in an organisation, using predictors such as the industry verticals, the candidate's skillsets, workplace location, gender, compensation offered, and the notice period of the candidate. The model developed is a logistic regression model, to determine whether a candidate selected will accept a job opportunity in an organisation or not. The analysis was performed based on a sample of 443 candidates who were provided job offers in the period 2013-2015 by a recruitment service provider. [ABSTRACT FROM AUTHOR]

In this paper a family of Wald-type test statistics for linear hypotheses in the logistic regression model with complex sample survey data is introduced and its properties are explored. The family of tests considered is based on the pseudo minimum phi-divergence estimator that contains, as a particular case, the pseudo maximum likelihood estimator. We obtain the asymptotic distribution and through a simulation study it is shown that some Wald-type tests present much more stable levels than the classical one for high and moderate values of the intra-cluster correlation parameter. [ABSTRACT FROM AUTHOR]

Background: Molecular testing (MT) is commonly used to refine cancer probability in thyroid nodules with indeterminate cytology. Whether or not ultrasound (US) patterns and clinical parameters can further inform the risk of thyroid cancer in nodules predicted to be positive or negative by MT remains unknown. The aim of this study was to test if clinical parameters, including patient age, sex, nodule size (by US), Bethesda category (III, IV, V), US pattern (American Thyroid Association [ATA] vs. American College of Radiology Thyroid Image Reporting and Data System [TI-RADS] systems), radiation exposure, or family history of thyroid cancer can modify the probability of thyroid cancer or noninvasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP) predicted by MT. Methods: We studied 257 thyroid nodules in 232 patients from 10 study centers with indeterminate fine needle aspiration cytology and informative MT results using the ThyroSeq v3 genomic classifier (TSv3). Univariate and multivariate logistic regression was used for data analysis. Results: The presence of cancer/NIFTP was associated with positive TSv3 results (odds ratio 61.39, p < 0.0001). On univariate regression, patient sex, age, and Bethesda category were associated with cancer/NIFTP probability (p < 0.05 for each). Although ATA (p = 0.1211) and TI-RADS (p = 0.1359) US categories demonstrated positive trends, neither was significantly associated with cancer/NIFTP probability. A multivariate regression model incorporating the four most informative non-MT covariates (sex, age, Bethesda category, and ATA US pattern; Model No. 1) yielded a C index of 0.653; R2 = 0.108. When TSv3 was added to Model number 1, the C index increased to 0.888; R2 = 0.572. However, age (p = 0.341), Bethesda category (p = 0.272), and ATA US pattern (p = 0.264) were nonsignificant, and other than TSv3 (p < 0.0001), male sex was the only non-MT parameter that potentially contributed to cancer/NIFTP risk (p = 0.095). The simplest and most efficient clinical model (No. 3) incorporated TSv3 and sex (C index = 0.889; R2 = 0.588). Conclusions: In this multicenter study of thyroid nodules with indeterminate cytology and MT, neither the ATA nor TI-RADS US scoring systems further informed the risk of cancer/NIFTP beyond that predicted by TSv3. Although age and Bethesda category were associated with cancer/NIFTP probability on univariate analysis, in sequential nomograms they provided limited incremental value above the high predictive ability of TSv3. Patient sex may contribute to cancer/NIFTP risk in thyroid nodules with indeterminate cytology. [ABSTRACT FROM AUTHOR]

Taking Shandong Province as the research area, we explored the willingness of farmers to grow grain and the influencing factors. By constructing the evaluation system of their willingness with 6 levels and 15 indicators based on field investigation, and quantitatively analyzing the influence degree and impact assessment of factors through a logistic regression model, the regional differences in farmers’ willingness were summarized, and the influencing factors were recognized. This study indicates that there were obvious regional differences in farmers’ willingness, which were the highest in the western region, the second in the eastern region, and the lowest in the central region. Specifically, the willingness varies significantly among cities, among which Laiwu has the highest willingness (0.76), while Tai’an has the lowest (0.41). The level of urbanization in different regions and the main influencing factors are different, and the same factor has different degrees of influence on cities, leading to regional differences. In terms of urbanization level, the main influencing factors in areas with high urbanization levels are the proportion of grain income and grain expenditure. However, in areas with a low urbanization level, it is the farmers’ planting attitude. From the perspective of influence mode, different factors have positive and negative differences in the willingness. Additionally, farmers’ willingness is becoming more and more rational, and more consideration is given to economic benefits. Among the influencing factors, the land planting mode, the proportion of grain income, and the proportion of grain expenditure are the most important factors, and 82% (11) of the cities are affected by the above three factors. Finally, the corresponding incentive measures are proposed by the regional differences in the influencing factors in various cities.

This study explored the respective importance of compositional (individual) and contextual (neighbourhood) factors associated with the propensity to engage in formal volunteering among a nationally representative sample of adults in Wales, UK. To date, while certain contextual characteristics of local communities have been found to be associated with the propensity to volunteer, compositional characteristics of residents tend to be stronger predictors. Few studies to date have specifically explored associations with local volunteering opportunities. To address such gaps, this study examined the extent to which such opportunities and broader neighbourhood factors such as urban/rural status and deprivation impacted upon propensities to volunteer, adjusting for important compositional predictors of voluntarism. In summary, while volunteering was marginally associated with a measure of local voluntary opportunities, hinting that the odds of formal volunteering are greater among those living in areas with more local voluntary organizations, this association was not retained following adjustment for other factors. This suggests that much of the area-level variance is explained by spatial variations in compositional factors. Further research is needed to examine determinants of volunteering behaviour at a range of spatial scales by drawing on wider measures of volunteering opportunities, before the factors at play can be fully understood. [ABSTRACT FROM AUTHOR]

Successful implementation of enzymes in practical application hinges on the development of efficient mass production techniques. However, in a heterologous expression system, the protein is often unable to fold correctly and, thus, forms inclusion bodies, resulting in the loss of its original activity. In this study, we present a new and more accurate model for predicting amino acids associated with an increased L-amino acid oxidase (LAO) solubility. Expressing LAO from Rhizoctonia solani in Escherichia coli and combining random mutagenesis and statistical logistic regression, we modified 108 amino acid residues by substituting hydrophobic amino acids with serine and hydrophilic amino acids with alanine. Our results indicated that specific mutations in Euclidean distance, glycine, methionine, and secondary structure increased LAO expression. Furthermore, repeated mutations were performed for LAO based on logistic regression models. The mutated LAO displayed a significantly increased solubility, with the 6-point and 58-point mutants showing a 2.64- and 4.22-fold increase, respectively, compared with WT-LAO. Ultimately, using recombinant LAO in the biotransformation of α-keto acids indicates its great potential as a biocatalyst in industrial production., (© 2024 Wiley-VCH GmbH.)

In proof-of-payment transit systems, fare evasion has recently captured increasing attention because of the relevant implications it produces. Research has investigated how sociodemographic, travel behaviour, and situational determinants affect the intention to evade fares for segments of passengers clustered according to ‘Gender’ and ‘Age’. Conversely, no study has isolated these determinants in segments clustered according to ‘Employment’. This paper fills this gap by analyzing students, workers, and unemployed passengers. Key determinants are isolated by logistic regression models. The findings show that gender, age, and having been fined are the common determinants that make all these segments more likely to evade fares. In addition, some specific determinants are identified for each segment. Hence, the overall findings may support transit operators by anticipating preventive and corrective strategies tailored to specific segments, which can positively impact other segments.

An early warning system to timely forecast turbulences in the Asian and European stockmarket is proposed. To ensure comparability, the model is constructed analogously to the early warningsystem for the US stock market presented by Hauptmann et al. (2014). Based on the time series ofdiscrete monthly returns of the Nikkei 225 and the EuroStoxx 50, filtered probabilities are estimated bytwo successive Markov-switching models with two regimes each. The market is thus separated in threestates: calm, turbulent positive and turbulent negative. Subsequently, a forecasting model using logisticregression and economic input factors is selected. In an empirical asset management case study it isillustrated that the investment performance is improved when considering the signals of the establishedwarning system. Moreover, the US, Asian and European model are compared and interdependenciesare highlighted.

Background: Although conventional phaco and femtosecond laser-assisted cataract surgery are widely used treatment options, patients undergoing these approaches often experience peri- and post-operative complications that worsen functional outcomes. Cystic macular edema is one of these complications and occurs in 1-28% of patients. Purpose: To assess the efficacy of surgical treatment for age-related cataract through risk factor analysis using logistic regression models. Materials and Methods: Eighty-three eyes of 83 patients with age-related cataract (lens nuclei of grades 1 to 3, Buratto classification scheme) participated in the study. Aqueous humor samples were collected during phacoemulsification procedures and investigated for the presence of prostanoids, thromboxane B2 and prostacyclin 6-keto-PGF1α. A negative outcome of surgical treatment for cataract was defined as development of macular edema; any other outcome was considered positive. Results: 6-keto-PGF1α/ thromboxane B2 ratio can be used as a marker of the risk for developing macular edema 1 year after phacoemulsification for age-related cataract.

Pancreatic ductal adenocarcinoma (PDAC) is often diagnosed at a late, incurable stage. We sought to determine whether individuals at high risk of developing PDAC could be identified early using routinely collected data. Electronic health record (EHR) databases from two independent hospitals in Boston, Massachusetts, providing inpatient, outpatient, and emergency care, from 1979 through 2017, were used with case–control matching. PDAC cases were selected using International Classification of Diseases 9/10 codes and validated with tumour registries. A data-driven feature selection approach was used to develop neural networks and L2-regularised logistic regression (LR) models on training data (594 cases, 100,787 controls) and compared with a published model based on hand-selected diagnoses ('baseline'). Model performance was validated on an external database (408 cases, 160,185 controls). Three prediction lead times (180, 270 and 365 days) were considered. The LR model had the best performance, with an area under the curve (AUC) of 0.71 (confidence interval [CI]: 0.67–0.76) for the training set, and AUC 0.68 (CI: 0.65–0.71) for the validation set, 365 days before diagnosis. Data-driven feature selection improved results over 'baseline' (AUC = 0.55; CI: 0.52–0.58). The LR model flags 2692 (CI 2592–2791) of 156,485 as high risk, 365 days in advance, identifying 25 (CI: 16–36) cancer patients. Risk stratification showed that the high-risk group presented a cancer rate 3 to 5 times the prevalence in our data set. A simple EHR model, based on diagnoses, can identify high-risk individuals for PDAC up to one year in advance. This inexpensive, systematic approach may serve as the first sieve for selection of individuals for PDAC screening programs. • Medical records can be used to identify people at high risk for pancreatic cancer. • The high-risk group identified 6–12 months before diagnosis, allowing early detection. • A data-driven approach is superior to hand-selected features for model prediction. • External validation of the model shows generalisability to new data. [ABSTRACT FROM AUTHOR]

Objective: To analyze the changes in the serum levels of CRH and 5‐HT in women with postpartum depression (PPD) and to study the value of the CRH/5‐HT ratio for the prediction of PPD. Methods: This prospective study recruited pregnant women from the Fourth Affiliated Hospital, China Medical University between January 2017 and October 2019. Women were considered for inclusion if they had no history, or no current evidence, of a psychiatric disorder. All women were assessed at postpartum day 10 with the Edinburgh Postnatal Depression Scale (EPDS). Blood samples were obtained at 20 weeks of pregnancy and the levels of CRH and 5‐HT were determined by radioimmunoassay and ELISA. Associations between EPDS score, the demographic variables, and hormone levels were identified using bivariate logistic regression models. Results: A total of 185 women were included. We found that the serum level of both CRH and 5‐HT was significantly correlated with EPDS score; the AUC for CRH was 0.79, and 5‐HT was 0.85, which indicated that both CRH and 5‐HT are a reliable biomarker for PPD. The AUC, specificity, and sensitivity of CRH/5‐HT were 0.92, 0.86, and 0.95, respectively, which were better than those of CRH or 5‐HT individually. Conclusions: We believe that the serum CRH/5‐HT ratio is an excellent biomarker for the prediction of PPD. Serum CRH/5‐HT ratio was more reliable at predicting postpartum depression than serum CRH or 5‐HT levels individually. [ABSTRACT FROM AUTHOR]

Partial discharge (PD) investigations can identify and localise incipient failures in power transformers early, thus avoiding considerable financial losses. The feature extraction of PD signals is a fundamental step for the development of such location techniques since it directly influences the performance of a location method. This study presents a detailed comparative analysis of four traditional approaches for the obtaining of attributes towards a better set of signal features for the location of PDs. The approaches were critically compared regarding their ability to locate experimentally generated discharges between adjacent layers of a prototype winding. In order to perform such analysis, a localisation structure based on logistic regression models was elaborated, capable of determining both layers and sections of the winding affected by PDs and easily applicable in practice. The results show energy features of wavelet coefficients, obtained through the decomposition of high‐frequency current signals acquired at the winding endings, achieve better performance in the PD localisation, accurately indicating discharge occurrence points among layers and sections of the winding. [ABSTRACT FROM AUTHOR]

The effects of meteorological conditions around silking (Si) and physiological maturity (PM) on production of fumonisins (FB=FB1 + FB2) in maize kernels were analyzed. Kernel FB contents were determined on kernel samples collected from an experimental susceptible hybrid (n = 35) and a Bt commercial hybrid (n = 23) planted in several growing seasons/sites from the Argentinean Pampas. Considering the effect of genetic divergence of the two maize hybrids, total kernel FB concentrations (n = 52) binary coded, were predicted appropriately by weather-based logistic regression models but underestimated in some samples severely contaminated with FB. After removing these misclassified cases that registered maximum values of a drought-heat stress index (DI) calculated over 30 days around Si, and weather conditions (assessed by weather interactive components) conducive to infection/production of FB in PM, new logistic models were fitted. These new models improved their predictive ability indices. It was remarkable a model including the discrete genetic variable and the weather variable associated with the simultaneous occurrence of precipitation, temperature between 19.5–33 °C and relative humidity >70%, required for fungal infection in Si. Models that also included weather variables calculated in PM and associated with the kernel drying rate and fungal infection, did not result in better prediction outcomes. Opposite to the general trend, the occurrence of both severe heat-drought stress around Si and favorable weather around PM led to high kernel FB contents. We hypothesize that husk shortening by stress at silking might expose ears, promoting Fusarium verticillioides colonization/FB synthesis in late stages of kernel development and maturity, whenever favorable environmental conditions for both processes prevail. [ABSTRACT FROM AUTHOR]

Salmonella Heidelberg and Salmonella Typhimurium are among the most common serotypes responsible for human salmonellosis in Ontario. Introduction of the Ontario Investigation Tools (OIT) in 2014 allowed for standardized case investigation and reporting. This study compared the risk factors and symptomatology for sporadic S. Heidelberg and S. Typhimurium cases reported in Ontario in 2015, following implementation of the OIT. Multilevel logistic regression models were applied to assess associations between serotype and individual‐level demographic characteristics, exposures and symptoms for sporadic confirmed cases of S. Heidelberg and S. Typhimurium in Ontario in 2015. There were 476 sporadic cases of S. Typhimurium (n = 278) and S. Heidelberg (n = 198) reported in Ontario in 2015. There were significant associations between the odds of the isolate from a case being one of these serotypes, and travel, consumption of sprouts (any type), contact with reptiles and development of malaise, fever or bloody diarrhoea. The S. Typhimurium and S. Heidelberg cases differed in both symptom presentation and risk factors for illness. Case–case comparisons of Salmonella serotypes have some advantages over case–control studies in that these are less susceptible to selection and recall bias while allowing for rapid comparison of cases to identify potential high‐risk exposures that are unique to one of the serotypes when compared to the other. Comparing cases of two different Salmonella serotypes can help to highlight risk factors that may be uniquely associated with one serotype, or more strongly associated with one serotype compared to another. This information may be useful for understanding relative source attribution between common serotypes of Salmonella. [ABSTRACT FROM AUTHOR]

This study analyses individual-level determinants of internet banking adoption of customers in Turkey. Using a nationally representative household survey data, logistic regression models are estimated for quantification of the factors which influence consumer's decision of internet banking adoption. Empirical results indicate that females are less likely to use internet banking compared to males in Turkey. Education level, being employed, household income level, frequency, variety and skill levels of internet usage have significantly positive associations with likelihood of using internet banking services. Additionally, age demonstrates a non-linear association with the use of internet banking. Middle-aged Turkish citizens are more likely to employ internet banking tools compared to the young and the elderly. Hence, we conclude that the digital divide exists in the Turkish case and elimination of disparities in technology adoption has the potential to bring substantial benefits to the financial system in Turkey. [ABSTRACT FROM AUTHOR]

Early identification of learning difficulties is a critical component of the Response to Intervention (RtI) model. In kindergarten, the screening of foundational reading skills can provide a data-based guideline for identifying students requiring a more intensive response-based intervention before starting elementary school. This study examines the classification accuracy and best predictors of a set of Spanish curriculum-based measures administered during kindergarten. The study's sample included 189 students tested in the fall, winter, and spring. Receiver operating characteristic (ROC) analysis was conducted. The composite score of the curriculum-based measurement (CBM) revealed area under the ROC curve (AUC) values of 0.83, 0.97, and 0.94 in the fall, winter, and spring, respectively. Phonemic awareness and letter-sound knowledge were the only isolated measures that demonstrated excellent AUC values throughout kindergarten. Logistic regression models showed that, when entered simultaneously, all measures were significant predictors of reading risk at some moment of the school year. [ABSTRACT FROM AUTHOR]

Background: The current study is aimed at identifying the factors associated with antimicrobial drug (AMD) use and stewardship practices on conventional California (CA) dairies a year after CA Senate Bill 27. Methods: Responses from 113 out of 1282 dairies mailed a questionnaire in 2019 were analyzed to estimate the associations between management practices and six outcomes including producer familiarity with medically important antimicrobial drugs (MIADs), restricted use of MIADs previously available over the counter (OTC), use of alternatives to AMD, changes in on-farm management practices, changes in AMD costs, and animal health status in dairies. Results: Producers who reported having a veterinarian–client–patient relationship (VCPR) and tracking AMD withdrawal intervals had greater odds of being familiar with the MIADs. Producers who began or increased the use of preventive alternatives to AMD in 2019 had higher odds (OR = 3.23, p = 0.04) of decreased use of MIADs previously available OTC compared to those who did not. Changes in management practices to prevent disease outbreak and the use of diagnostics to guide treatment were associated with producer-reported improved animal health. In addition, our study identified record keeping (associated with familiarity with MIADs), use of alternatives to AMD (associated with management changes to prevent diseases and decreased AMD costs), and use of diagnostics in treatment decisions (associated with reported better animal health) as factors associated with AMD stewardship. Conclusions: Our survey findings can be incorporated in outreach education materials to promote antimicrobial stewardship practices in dairies.

Abstract Background Negative Pressure Wound Therapy (NPWT) is being increasingly used to treat postoperative infections after osteosynthetic fracture fixation. The aim of the present study was to analyze the influence of epidemiological and microbiological parameters on outcome. Methods Infections following operative fracture fixation were registered in a comprehensive Critical Incidence Reporting System and subsequently analyzed retrospectively for characteristics of patients including comorbidity, bacteria, and clinical factors. The influence of the investigated parameters was analyzed using logistic regression models based on data from 106 patients. Results Staged wound lavage in combination with NPWT allowed implant preservation in 44% and led to successful healing in 73% of patients. Fermentation characteristics, load and behavior after gram staining revealed no statistically significant correlation with either healing or implant preservation. Infecting bacteria were successfully isolated in 87% of patients. 20% of all infections were caused by bacterial combinations. We observed a change in the infecting bacterial species under therapy in 23%. Age, gender, metabolic diseases or comorbidities did not influence the probability of implant preservation or healing. The delayed manifestation of infection (>4 weeks) correlated with a higher risk for implant loss (OR 5.1 [95% CI 1.41–17.92]) as did the presence of bacterial mixture (OR 5.0 [95% CI 1.41–17.92]) and open soft-tissue damage ≥ grade 3 (OR 10.2 [CI 1.88–55.28]). Wounds were less likely to heal in conjunction with high CRP blood levels (>20 mg/l) at the time of discharge (OR 3.6 [95% CI 1.31–10.08]) or following a change of the infecting bacterial species under therapy (OR 3.2 [95% CI, 1.13–8.99]). Conclusions These results indicate that the delayed manifestation of infection, high CRP blood levels at discharge, and alterations in the infecting bacterial species under therapy raise the risk of NPWT failure.

Objective: To find a suitable dividing value to classify cystatin C and evaluate the association between cognition and levels of cystatin C. Methods: Using data from the China Health and Retirement Longitudinal Study, We conducted a longitudinal analysis of a prospective cohort of 6,869 middle-aged and older Chinese without cognitive impairment at baseline. Levels of cystatin C were categorized into 2 groups by method of decision tree. Logistic regression models evaluated whether cystatin C was related to cognitive impairment. Results: Respondents were categorized as lower levels of cystatin C and higher levels of cystatin C, cut-point was 1.11 mg/L. Higher levels of cystatin C was associated with the odds of cognitive impairment (OR, 1.56; 95% CI, 1.10-2.22) after multivariable adjustment. Respondents with higher levels of cystatin C had worse cognition scores. Conclusions: We found a suitable dividing value of cystatin C in middle-aged and older Chinese. [ABSTRACT FROM AUTHOR]

Background and Purpose: Unstable pelvic fractures frequently require emergency stabilization using a C-clamp or external (CC/EF) fixation. However, the effectiveness of this intervention and associated complications are still a matter of debate.Patients and Methods: The analysis used data available from the German Pelvic Trauma Registry to study general complications, infections and mortality after primary stabilization using CC/EF in 5,499 patients (n = 957 with vs n = 4,542 without). Furthermore, the subgroups with secondary surgery (n = 713 vs n = 1,695), and ilio-sacral screw implantation following C-clamp stabilization were evaluated (n = 24 vs n = 219). Calculated odds ratios were adjusted for potential confounders.Results: Patients treated by CC/EF were younger (45 ± 20 vs 62 ± 24 years), had more C-type fractures (65% vs 28%), higher ISS (≥25 63% vs 20%) and displacement (≥3 mm 81% vs 41%), and more complex fractures (32% vs 5%). These features were independent risk factors for complications (p < 0.001). While mortality was reduced after CC/EF stabilization by 32% (OR 0.68 95%CI 0.49-0.95), the risk for general complications was slightly increased (OR 1.25 95% CI 1.02-1.53). In patients undergoing secondary surgery, CC/EF fixation had no influence on mortality, general complications or infections. Related to preceding C-clamp stabilization (OR 4.67 95% CI 1.06-20.64), the risk for infection increased from 3.2% to 20.8% in ilio-sacral screw fixation.Interpretation: Primary stabilization of unstable pelvic fractures with C-clamp or external fixation is associated with a decreased mortality and was not an independent risk factor for complications after secondary surgery. However, the risk for infection after ilio-sacral screw fixation increased almost 5-fold after C-clamp use. [ABSTRACT FROM AUTHOR]

Background: Exercise therapy is the key to preventing admission of patients with type 2 diabetes mellitus (T2DM). However, a few studies have examined the effects of exercise therapy on patients with T2DM undergoing inpatient diabetes self-management education and support (IDSMES)., Objective: This study investigated whether exercise therapy influenced the incidence of admission after discharge in patients with T2DM undergoing IDSMES., Methods: This retrospective cohort study included patients with T2DM who underwent IDSMES between June 2011 and May 2015. Overall, 258 patients were included in this study. The exercise therapy program was implemented in June 2013. Accordingly, patients diagnosed between June 2011 and May 2013 were categorized as the non-exercise therapy program group, while those diagnosed between June 2013 and May 2015 were categorized as the exercise therapy program group. Outcomes were incident diabetes-related and all-cause admissions within 1 year of discharge. Multiple logistic regression models were used to estimate the odds ratios (ORs) and 95% confidence intervals (CIs) of the exercise therapy program's impact on the outcomes., Results: Within 1 year of discharge, 27 (10.5%) patients underwent diabetes-related admissions and 62 (24.0%) underwent all-cause admissions. Multiple logistic regression analyses showed a significant association of the exercise therapy program with incident diabetes-related and allcause admissions [OR: 0.22 (95% CI: 0.08-0.59) and 0.44 (95% CI: 0.22-0.86), respectively]., Conclusion: Exercise therapy programs significantly lowered the incidences of diabetes-related and all-cause admissions. This indicates that implementing exercise therapy during hospitalization may be important for preventing admissions of patients with T2DM receiving IDSMES., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Objective: To externally validate the diagnostic and prognostic value of three previously identified microRNAs in emergency department patients with sepsis. Methods: Patients meeting consensus criteria for sepsis and septic shock were compared to controls. Three microRNAs (miR-150, miR-146a, and miR-223) were measured using real-time quantitative PCR, and levels of miRNAs were compared among the three cohorts. The association between miRNAs and both inflammatory markers and Sequential Organ Failure Assessment (SOFA) score were compared. To assess the prognostic value of each miRNA, unadjusted and adjusted logistic regression models were constructed using in-hospital mortality as the dependent variable. Results: Ninety-three patients were enrolled; 24 controls, 29 with sepsis, and 40 with septic shock. We found no difference in serum plasma miR-146a or miR-223 between cohorts, and found no association among these microRNAs and either inflammatory markers or SOFA score. miR-150 demonstrated a significant correlation with SOFA score (P= 0.31, P=0.01) and IL-10 (P=0.37, P=0.001), but no IL-6 or TNF-α (P=0.046, P=0.59). Logistic regression demonstrated miR-150 to be independently associated with mortality, even after adjusting for SOFA score (P=0.003) or initial lactate (P=0.01). Conclusions: miR-146a and miR-223 demonstrated no significantly diagnostic or prognostic ability in this cohort. miR-150 was associated with inflammation, severity of illness, and mortality. Given the independent predictive value of miR-150, additional research regarding its role in sepsis is warranted.

Background: Negative Pressure Wound Therapy (NPWT) is being increasingly used to treat postoperative infections after osteosynthetic fracture fixation. The aim of the present study was to analyze the influence of epidemiological and microbiological parameters on outcome.Methods: Infections following operative fracture fixation were registered in a comprehensive Critical Incidence Reporting System and subsequently analyzed retrospectively for characteristics of patients including comorbidity, bacteria, and clinical factors. The influence of the investigated parameters was analyzed using logistic regression models based on data from 106 patients.Results: Staged wound lavage in combination with NPWT allowed implant preservation in 44% and led to successful healing in 73% of patients. Fermentation characteristics, load and behavior after gram staining revealed no statistically significant correlation with either healing or implant preservation. Infecting bacteria were successfully isolated in 87% of patients. 20% of all infections were caused by bacterial combinations. We observed a change in the infecting bacterial species under therapy in 23%. Age, gender, metabolic diseases or comorbidities did not influence the probability of implant preservation or healing. The delayed manifestation of infection (>4 weeks) correlated with a higher risk for implant loss (OR 5.1 [95% CI 1.41-17.92]) as did the presence of bacterial mixture (OR 5.0 [95% CI 1.41-17.92]) and open soft-tissue damage ≥ grade 3 (OR 10.2 [CI 1.88-55.28]). Wounds were less likely to heal in conjunction with high CRP blood levels (>20 mg/l) at the time of discharge (OR 3.6 [95% CI 1.31-10.08]) or following a change of the infecting bacterial species under therapy (OR 3.2 [95% CI, 1.13-8.99]).Conclusions: These results indicate that the delayed manifestation of infection, high CRP blood levels at discharge, and alterations in the infecting bacterial species under therapy raise the risk of NPWT failure. [ABSTRACT FROM AUTHOR]

Purpose: To propose a predictive model to aid in the decision to perform the intravitreal anti-VEGF injection, based on the risk factors quantification and hierarchy presented by diabetic patients. Methods: It is a cross-sectional, observational and inferential study carried out in three institutions in Paraíba from July 2015 to September 2016. The logistic regression model was used to obtain the predictive model and data were analyzed in R® software. Results: Eighty patients with type 1 or 2 diabetes, over 18 years of age, were included, 57.5% of whom had no indication of IIV and 42.5% received an indication of this treatment. In the group with diabetic macular edema (DME), the mean age was 60.65 years, of which 52.94% were female. In this group, the majority presented severe non-proliferative diabetic retinopathy or proliferative retinopathy (79.41%). The main risk factors for DME were: be retired (OR = 5.22, p-value0.05), had a personal history of diabetic retinopathy (OR = 20.27, p-value 0.006), and previous treatment with anti-VEGF (OR = 23.23, p-value 0.002). Conclusion: The results of the research showed that a diabetic patient with low visual acuity and presenting these three factors should be referred as soon as possible to the specialist, since he presents a risk of presenting DME with need for anti-VEGF IIV, with 91.17% of accuracy. This tool can serve as an adjunct to decision making, especially the nonretinologist, in order to refer individuals with EMD to early diagnosis and treatment, which may be crucial in preventing irreversible visual loss in these patients. [ABSTRACT FROM AUTHOR]

Logistic regression models were developed to identify significant factors that influence the location of existing wood-using bioenergy/biofuels plants and traditional wood-using facilities. Logistic models provided quantitative insight for variables influencing the location of woody biomass-using facilities. Availability of “thinnings to a basal area of 31.7m2/ha,” “availability of unused mill residues,” and “high density of railroad availability” had positive significant influences on the location of all wood-using faciities. “Median family income,” “population,” “low density of railroad availability,” and “harvesting costs for logging residues” had negative significant influences on the location of all wood-using faciities. For larger woody biomass-using mills (e.g., biopower) availability of “thinnings to a basal area of 79.2m2/ha,” “number of primary and secondary wood-using mills within an 128.8km haul distance,” and “amount of total mill residues,” had positive significant influences on the location of larger wood-using faciities. “Population” and “harvesting costs for logging residues” have negative significant influences on the location of larger wood-using faciities. Based on the logistic models, 25 locations were predicted for bioenergy or biofuels plants for a 13-state study region in the Southern United States.

The purpose of this study was to examine physiological and physical determinants of ice-hockey performance in order to assess their impact on the result during a selection for ice hockey. A total of 42 ice hockey players took part in the selection camp. At the end of the camp 20 best players were selected by team of expert coaches to the ice hockey team and created group G1, while the second group (G2) consisted of not selected players (non-successful group Evaluation of goodness of fit of the model to the data was based on the Hosmer Lemeshow test. Ice hockey players selected to the team were taller 181.95±4.02 cm, had lower % body fat 13.17±3.17%, a shorter time to peak power 2.47±0.35 s, higher relative peak power 21.34±2.41 W ⋅ kg-1 and higher relative total work 305.18±28.41 J ⋅ kg-1. The results of the aerobic capacity test showed significant differences only in case of two variables. Ice hockey players in the G1 had higher VO2max 4.07±0.31 l ⋅ min-1 values than players in the G2 as well as ice hockey players in G1 showed a higher level of relative VO2max 51.75±2.99 ml ⋅ min-1 ⋅ kg-1 than athletes in G2. Ice hockey players selected to the team (G1) performed better in the 30 m Forwards Sprint 4.28±0.31 s; 6x9 Turns 12.19±0.75 s; 6x9 stops 12.79±0.49 s and Endurance test (6x30 m stops) 32.01±0.80 s than players in G2. The logistic regression model showed that the best predictors of success in the recruitment process of top level ice hockey players were time to peak power, relative peak power, VO2max and 30 m sprint forwards on ice. On the basis of the constructed predictive logistic regression model it will be possible to determine the probability of success of the athletes during following the selection processes to the team. [ABSTRACT FROM AUTHOR]