The Relationship of Level of Education to Social Reintegration after Burn Injury: A LIBRE Study

2019 ◽  
Vol 40 (5) ◽  
pp. 696-702
Author(s):  
John T Schulz ◽  
Gabriel D Shapiro ◽  
Amy Acton ◽  
Philip Fidler ◽  
Molly E Marino ◽  
...  
Keyword(s):  
At Risk ◽  
Regression Models ◽  
Burn Injury ◽  
Graduate Degree ◽  
Data Set ◽  
Level Of Education ◽  
Social Reintegration ◽  
Entire Cohort ◽  
Scale Scores ◽  
Social Recovery

Abstract Social and emotional recovery from burn injury is a complex process impacted by both clinical and social factors. Because level of education (LOE) has been correlated to overall health, health outcomes, and life expectancy, we questioned whether LOE might be associated with successful social recovery after burn injury. The Life Impact Burn Recovery Evaluation (LIBRE) data set served as a novel tool to explore this question. The LIBRE project is a collaborative effort designed to provide a clinical yardstick for social reintegration among burn survivors. After institutional review board approval, 601 burn survivor respondents, aged 18 or over with >5% TBSA burn were surveyed and a six-scale, 126-item LIBRE Profile was derived from their responses. LOE was collapsed into four categories ranging from less than high school equivalency certificate to graduate degree. Impact of burn injury on subsequent LOE was examined by splitting the sample into those burned at age 30 years or less and those burned at greater than 30 years of age. Regression models were run to estimate associations between education and scale scores with adjustment for age at injury, sex, marital status, work status, TBSA, and time since burn. Regression models were run on the entire cohort and then stratified by age at burn injury (≤30 vs >30). Among all subjects, we found an association between LOE and social recovery as measured by LIBRE scale scores. This association was contributed entirely from the cohort burned at age 30 or less: for those burned at greater than age 30, there was no association between LOE and social recovery. Of particular interest, the distribution of LOE among those burned at ≤ 30 was very similar to LOE distribution in both millennials and in the U.S. population at large. LOE appears to be associated with social recovery for those burned at younger ages but not for those burned at over age 30. More importantly, burn injury during schooling may have no impact on a survivor’s educational trajectory since distribution of LOE in our ≤30 cohort mirrors that of the general population. LOE and age at burn injury may provide a quick screen for survivors at risk of difficult social reintegration, allowing providers to target those at risk with additional peer support and counseling.

Challenging the Link Between Early Childhood Television Exposure and Later Attention Problems: A Multiverse Approach

2021 ◽  
pp. 095679762097165
Author(s):  
Matthew T. McBee ◽  
Rebecca J. Brand ◽  
Wallace E. Dixon
Keyword(s):  
Early Childhood ◽  
Regression Models ◽  
Propensity Score Analysis ◽  
Harmful Effect ◽  
Attention Problems ◽  
Linear Regression Models ◽  
Data Set ◽  
Logistic Regression Models ◽  
Score Analysis ◽  
Television Exposure

In 2004, Christakis and colleagues published an article in which they claimed that early childhood television exposure causes later attention problems, a claim that continues to be frequently promoted by the popular media. Using the same National Longitudinal Survey of Youth 1979 data set ( N = 2,108), we conducted two multiverse analyses to examine whether the finding reported by Christakis and colleagues was robust to different analytic choices. We evaluated 848 models, including logistic regression models, linear regression models, and two forms of propensity-score analysis. If the claim were true, we would expect most of the justifiable analyses to produce significant results in the predicted direction. However, only 166 models (19.6%) yielded a statistically significant relationship, and most of these employed questionable analytic choices. We concluded that these data do not provide compelling evidence of a harmful effect of TV exposure on attention.

Association Between Opioid Use and Diabetes in Patients With Ankle Fracture Repair

2021 ◽  
pp. 107110072110581
Author(s):  
Wenye Song ◽  
Naohiro Shibuya ◽  
Daniel C. Jupiter
Keyword(s):  
Diabetes Mellitus ◽  
Logistic Regression ◽  
Ankle Fracture ◽  
Regression Models ◽  
Fracture Repair ◽  
Opioid Use ◽  
Data Set ◽  
Opioid Prescription ◽  
Logistic Regression Models ◽  
Patients With Diabetes

Background: Ankle fractures in patients with diabetes mellitus have long been recognized as a challenge to practicing clinicians. Ankle fracture patients with diabetes may experience prolonged healing, higher risk of hardware failure, an increased risk of wound dehiscence and infection, and higher pain scores pre- and postoperatively, compared to patients without diabetes. However, the duration of opioid use among this patient cohort has not been previously evaluated. The purpose of this study is to retrospectively compare the time span of opioid utilization between ankle fracture patients with and without diabetes mellitus. Methods: We conducted a retrospective cohort study using our institution’s TriNetX database. A total of 640 ankle fracture patients were included in the analysis, of whom 73 had diabetes. All dates of opioid use for each patient were extracted from the data set, including the first and last date of opioid prescription. Descriptive analysis and logistic regression models were employed to explore the differences in opioid use between patients with and without diabetes after ankle fracture repair. A 2-tailed P value of .05 was set as the threshold for statistical significance. Results: Logistic regression models revealed that patients with diabetes are less likely to stop using opioids within 90 days, or within 180 days, after repair compared to patients without diabetes. Female sex, neuropathy, and prefracture opioid use are also associated with prolonged opioid use after ankle fracture repair. Conclusion: In our study cohort, ankle fracture patients with diabetes were more likely to require prolonged opioid use after fracture repair. Level of Evidence: Level III, prognostic.

How local personal vote-earning attributes affect the aggregate party vote share: Evidence from the Belgian flexible-list PR system (2003–2014)

Politics ◽  
2018 ◽  
Vol 39 (4) ◽  
pp. 464-479
Author(s):  
Gert-Jan Put ◽  
Jef Smulders ◽  
Bart Maddens
Keyword(s):  
Regression Models ◽  
Empirical Test ◽  
Original Data ◽  
Ordinary Least Squares ◽  
Vote Share ◽  
District Level ◽  
National Party ◽  
Data Set ◽  
Geographically Distributed ◽  
Party Vote Shares

This article investigates the effect of candidates exhibiting local personal vote-earning attributes (PVEA) on the aggregate party vote share at the district level. Previous research has often assumed that packing ballot lists with localized candidates increases the aggregate party vote and seat shares. We present a strict empirical test of this argument by analysing the relative electoral swing of ballot lists at the district level, a measure of change in party vote shares which controls for the national party trend and previous party results in the district. The analysis is based on data of 7527 candidacies during six Belgian regional and federal election cycles between 2003 and 2014, which is aggregated to an original data set of 223 ballot lists. The ordinary least squares (OLS) regression models do not show a significant effect of candidates exhibiting local PVEA on relative electoral swing of ballot lists. However, the results suggest that ballot lists do benefit electorally if candidates with local PVEA are geographically distributed over different municipalities in the district.

scholarly journals Return Based Risk Measures for Non-Normally Distributed Returns: An Alternative Modelling Approach

2021 ◽  
Vol 14 (11) ◽  
pp. 540
Author(s):  
Eyden Samunderu ◽  
Yvonne T. Murahwa
Keyword(s):  
At Risk ◽  
Normal Distribution ◽  
Hedge Funds ◽  
Value At Risk ◽  
Financial Risk ◽  
Risk Measures ◽  
Risk Measure ◽  
Extensive Literature ◽  
Data Set ◽  
Normally Distributed

Developments in the world of finance have led the authors to assess the adequacy of using the normal distribution assumptions alone in measuring risk. Cushioning against risk has always created a plethora of complexities and challenges; hence, this paper attempts to analyse statistical properties of various risk measures in a not normal distribution and provide a financial blueprint on how to manage risk. It is assumed that using old assumptions of normality alone in a distribution is not as accurate, which has led to the use of models that do not give accurate risk measures. Our empirical design of study firstly examined an overview of the use of returns in measuring risk and an assessment of the current financial environment. As an alternative to conventional measures, our paper employs a mosaic of risk techniques in order to ascertain the fact that there is no one universal risk measure. The next step involved looking at the current risk proxy measures adopted, such as the Gaussian-based, value at risk (VaR) measure. Furthermore, the authors analysed multiple alternative approaches that do not take into account the normality assumption, such as other variations of VaR, as well as econometric models that can be used in risk measurement and forecasting. Value at risk (VaR) is a widely used measure of financial risk, which provides a way of quantifying and managing the risk of a portfolio. Arguably, VaR represents the most important tool for evaluating market risk as one of the several threats to the global financial system. Upon carrying out an extensive literature review, a data set was applied which was composed of three main asset classes: bonds, equities and hedge funds. The first part was to determine to what extent returns are not normally distributed. After testing the hypothesis, it was found that the majority of returns are not normally distributed but instead exhibit skewness and kurtosis greater or less than three. The study then applied various VaR methods to measure risk in order to determine the most efficient ones. Different timelines were used to carry out stressed value at risks, and it was seen that during periods of crisis, the volatility of asset returns was higher. The other steps that followed examined the relationship of the variables, correlation tests and time series analysis conducted and led to the forecasting of the returns. It was noted that these methods could not be used in isolation. We adopted the use of a mosaic of all the methods from the VaR measures, which included studying the behaviour and relation of assets with each other. Furthermore, we also examined the environment as a whole, then applied forecasting models to accurately value returns; this gave a much more accurate and relevant risk measure as compared to the initial assumption of normality.

scholarly journals Clinically Applicable Segmentation of Head and Neck Anatomy for Radiotherapy: Deep Learning Algorithm Development and Validation Study

10.2196/26151 ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. e26151
Author(s):  
Stanislav Nikolov ◽  
Sam Blackwell ◽  
Alexei Zverovitch ◽  
Ruheena Mendes ◽  
Michelle Livne ◽  
...  
Keyword(s):  
Computed Tomography ◽  
At Risk ◽  
Deep Learning ◽  
Clinical Practice ◽  
Head And Neck ◽  
Organs At Risk ◽  
Data Set ◽  
Computed Tomography Scans ◽  
Neck Anatomy ◽  
Organ At Risk

Background Over half a million individuals are diagnosed with head and neck cancer each year globally. Radiotherapy is an important curative treatment for this disease, but it requires manual time to delineate radiosensitive organs at risk. This planning process can delay treatment while also introducing interoperator variability, resulting in downstream radiation dose differences. Although auto-segmentation algorithms offer a potentially time-saving solution, the challenges in defining, quantifying, and achieving expert performance remain. Objective Adopting a deep learning approach, we aim to demonstrate a 3D U-Net architecture that achieves expert-level performance in delineating 21 distinct head and neck organs at risk commonly segmented in clinical practice. Methods The model was trained on a data set of 663 deidentified computed tomography scans acquired in routine clinical practice and with both segmentations taken from clinical practice and segmentations created by experienced radiographers as part of this research, all in accordance with consensus organ at risk definitions. Results We demonstrated the model’s clinical applicability by assessing its performance on a test set of 21 computed tomography scans from clinical practice, each with 21 organs at risk segmented by 2 independent experts. We also introduced surface Dice similarity coefficient, a new metric for the comparison of organ delineation, to quantify the deviation between organ at risk surface contours rather than volumes, better reflecting the clinical task of correcting errors in automated organ segmentations. The model’s generalizability was then demonstrated on 2 distinct open-source data sets, reflecting different centers and countries to model training. Conclusions Deep learning is an effective and clinically applicable technique for the segmentation of the head and neck anatomy for radiotherapy. With appropriate validation studies and regulatory approvals, this system could improve the efficiency, consistency, and safety of radiotherapy pathways.

scholarly journals Measurement of Pharyngeal Residue From Lateral View Videofluoroscopic Images

2020 ◽  
Vol 63 (5) ◽  
pp. 1404-1415
Author(s):  
Catriona M. Steele ◽  
Melanie Peladeau-Pigeon ◽  
Ahmed Nagy ◽  
Ashley A. Waito
Keyword(s):  
Lateral View ◽  
Pyriform Sinus ◽  
Strong Correlations ◽  
Good Correspondence ◽  
Data Set ◽  
Frequency Distributions ◽  
Pharyngeal Residue ◽  
Housing Area ◽  
Kendall’S Τ ◽  
Scale Scores

Purpose The field lacks consensus about preferred metrics for capturing pharyngeal residue on videofluoroscopy. We explored four different methods, namely, the visuoperceptual Eisenhuber scale and three pixel-based methods: (a) residue area divided by vallecular or pyriform sinus spatial housing (“%-Full”), (b) the Normalized Residue Ratio Scale, and (c) residue area divided by a cervical spine scalar (%(C2–4) 2 ). Method This study involved retrospective analysis of an existing data set of videofluoroscopies performed in 305 adults referred on the basis of suspected dysphagia, who swallowed 15 boluses each (six thin and three each of mildly, moderately, and extremely thick 20% w/v barium). The rest frame at the end of the initial swallow of each bolus was identified. Duplicate measures of pharyngeal residue were made independently by trained raters; interrater reliability was calculated prior to discrepancy resolution. Frequency distributions and descriptive statistics were calculated for all measures. Kendall's τ b tests explored associations between Eisenhuber scale scores and pixel-based measures, that is, %-Full and %(C2–4) 2 . Cross-tabulations compared Eisenhuber scale scores to 25% increments of the %-Full measure. Spearman rank correlations evaluated relationships between the %-Full and %(C2–4) 2 measures. Results Complete data were available for 3,545 boluses: 37% displayed pharyngeal residue (thin, 36%; mildly thick, 41%; moderately thick, 35%; extremely thick, 34%). Eisenhuber scale scores showed modest positive associations with pixel-based measures but inaccurately estimated residue severity when compared to %-Full measures with errors in 20.6% of vallecular ratings and 14.2% of pyriform sinus ratings. Strong correlations ( p < .001) were seen between the %-Full and %(C2–4) 2 measures, but the %-Full measures showed inflation when spatial housing area was small. Conclusions Generally good correspondence was seen across different methods of measuring pharyngeal residue. Pixel-based measurement using an anatomical reference scalar, for example, (C2–4) 2 is recommended for valid, reliable, and precise measurement.

scholarly journals Comparing Spike and Slab Priors for Bayesian Variable Selection

2016 ◽  
Vol 40 (4) ◽  
Author(s):  
Gertraud Malsiner-Walli ◽  
Helga Wagner
Keyword(s):  
Variable Selection ◽  
Regression Models ◽  
Psychiatric Patients ◽  
Simulated Data ◽  
Bayesian Variable Selection ◽  
Data Set ◽  
Final Model ◽  
Mcmc Sampling ◽  
Regression Effect ◽  
Inclusion Probabilities

An important task in building regression models is to decide which regressors should be included in the final model. In a Bayesian approach, variable selection can be performed using mixture priors with a spike and a slab component for the effects subject to selection. As the spike is concentrated at zero, variable selection is based on the probability of assigning the corresponding regression effect to the slab component. These posterior inclusion probabilities can be determined by MCMC sampling. In this paper we compare the MCMC implementations for several spike and slab priors with regard to posterior inclusion probabilities and their sampling efficiency for simulated data. Further, we investigate posterior inclusion probabilities analytically for different slabs in two simple settings. Application of variable selection with spike and slab priors is illustrated on a data set of psychiatric patients where the goal is to identify covariates affecting metabolism.

Legal and Axiological Basis for Social Reintegration of Persons Sentenced to Imprisonment and Public Participation in the Process

2021 ◽  
Vol 13 (2) ◽  
pp. 5-6
Author(s):  
Przemysław Tarwacki
Keyword(s):  
At Risk ◽  
Social Exclusion ◽  
Negative Impact ◽  
Relevant Literature ◽  
Research Centre ◽  
The European Union ◽  
Social Reintegration ◽  
The Social ◽  
Depth Analysis ◽  
Polish Society

The article discusses the problem of social reintegration of prisoners, which — despite being raised many a time in the relevant literaturę — remains to be a point of issue. In the light of a recent survey conducted by the Polish Public Opinion Research Centre (hereinafter: CBOS), former convicts are considered by the Polish society as one of the groups of people most at risk of social exclusion. In turn, a report of the Ministry of Justice of 2020, regarding convicted adults, shows that a very large number of people leaving prison return to crime as early as in the first year after being released, which, for obvious reasons, has a negative impact on the internal security of our country. These circumstances encourage one to take a fresh glance at the problem of social reintegration of convicts and to search for additional arguments in favor of extending special support to this group of people. A review of the existing legislation indicates that it allows the principle of individualisation of assistance for the sake of social readaptation of individual convicts to be applied to an unlimited extent. What is strictly limited, however, is the circle of persons who can undertake activities for the social readaptation of prisoners during their imprisonment. The exclusion from the above-mentioned circle of all persons validly convicted of intentional offences is unjustified, and with regard to those members of society who, outside the structures of non-governmental organisations, wish to engage in activities for the social readaptation of convicted persons is downright unlawful, as it is contrary to higher-order legal acts. An in-depth analysis of the law in force leads to the conclusion that argumentation for not treating this social group differently from other individuals most at risk of social exclusion can be found in the constitution itself. On the other hand, a review of lower-order legal acts leads to the observation that since our country's accession to the European Union there have appeared both new measures and additional reasons, different from those traditionally identified in the doctrine of executive criminal law, for investing in any human capital in need of support, including persons sentenced to imprisonment.

Abstract TP75: Safety and Outcome of Thrombolysis in Acute Stroke with Prolonged Onset-to-Treatment Times and Low National Institute of Health Stroke Scale Scores. Report on Experience in China, First 1000 Patients of 3300 Treated

Stroke ◽  
2016 ◽  
Vol 47 (suppl_1) ◽  
Author(s):  
Pei-Lan Zhang ◽  
Jin-Huan Wang ◽  
Yu-Xin Wang ◽  
Yan Chen ◽  
Chen-Hao Zhang ◽  
...  
Keyword(s):  
Acute Stroke ◽  
Treatment Time ◽  
Significant Risk ◽  
Stroke Patients ◽  
Data Set ◽  
Scale Scores ◽  
Before And After ◽  
Metropolitan Hospital ◽  
Mri Scans ◽  
Efficacy Of Treatment

Introduction: Stroke in China is the leading cause of death. We report on a collaboration between a large metropolitan hospital in China, Tianjin Huanhu Hospital, and Inova Fairfax and Inova Alexandria Stroke Programs presenting data on the first 1000 (of more than 3300 treated since 2012) patients treated with IV-rtPA. The safety and efficacy of treatment with rtPA between 4.5-6 hours after onset is unclear. Similarly there is little data on outcome of patients treated with rtPA with normal or low NIHSS. Methods: Patients were treated with thrombolysis between late 2012 and fall of 2014. Patients had MRI scans at 24 hours. Patients had NIHSS scores before and after treatment, and modified Rankin Scores (mRS) at 90 days after treatment. Results: See Chart. Conclusions: 1) IV-rtPA can be given safely between 4.5-6 hours without significant risk of sICH and worsening outcomes. 2) Higher NIHSS before thrombolysis was correlated with poorer outcomes. 3) There was no significant correlation between onset to treatment time up to 6 hours and outcome. 4) Outcome was excellent (mRS 0-1) in 72% treated 0-3 hours, 74% 3-4.5 hours and remarkably 85% 4.5-6 hours. The data set includes many patients who were asymptomatic or nearly so prior to treatment. This will need to be more fully evaluated in the remainder (2300) of this cohort of more than 3300 treated patients. Comment: This cohort represents one of the largest series of acute stroke patients treated with IV-rtPA > 4.5 hours after onset. It also reveals outcome of treatment in patients with low NIHSS treated at various intervals after last know well.

A Risk Score Model Associated with Tumor Microenvironment Predicts Disease-Free Survival After Radical Prostatectomy

2020 ◽  
Author(s):  
Hao Zhao ◽  
Xuening Zhang ◽  
Zhan Shi ◽  
Songhe Shi
Keyword(s):  
Tumor Microenvironment ◽  
Gleason Score ◽  
Risk Score ◽  
Regression Models ◽  
Cox Regression ◽  
External Validation ◽  
Disease Free Survival ◽  
Data Set ◽  
Free Survival ◽  
Disease Free

Abstract Background Tumor microenvironment (TME) and immune checkpoint inhibitors has been shown to promote active immune responses through different mechanisms. We aimed to identify the important prognostic genes and prognostic characteristics related to TME in prostate cancer (PCa).Methods The gene transcriptome profiles and clinical information of PCa patients were obtained from the TCGA database, and the immune, stromal and estimate scores were calculated by the ESTIMATE algorithm. We evaluated the prognostic value of risk score (RS) model based on univariate Cox and LASSO Cox regression models analysis, and established a nomogram to predict disease-free survival (DFS) in PCa patients. The GSE70768 data set was used for external validation. Finally, 22 subsets of tumor-infiltrating immune cells (Tiics) were analyzed using the Cibersort algorithm.Results In this study, the patients with higher immune, stromal, and estimate scores were associated with poorer DFS, higher Gleason score, and higher AJCC T stage. Based on the immune and stromal scores, the Venny diagram screened out 515 cross DEGs. The univariate COX and Lasso Cox regression models were used to select 18 DEGs from 515 DEGs, and constructed a RS model. The DFS of the high-RS group was significantly lower than that of the low-RS group (P<0.001). The AUC of 1-year, 3-year and 5-year DFS rates in RS model were 0.778, 0.754 and 0.750, respectively. In addition, the RS model constructed from 18 genes was found to be more sensitive than Gleason score (1, 3, 5 year AUC= 0.704, 0.677 and 0.682). The nomograms of DFS were established based on RS and Gleason scores. The AUC of the nomograms in the first, third, and fifth years were 0.802, 0.808, and 0.796, respectively. These results have been further validated in GEO. In addition, the proportion of Tregs was higher in high-RS patients (P<0.05), and the expression of five immune checkpoints (CTLA-4, PD-1, LAG-3, TIM-3 and TIGIT) was higher in high-RS patients (P<0.05).Conclusion We identified 18 TME-related genes from the TCGA database, which were significantly related to DFS in PCa patients.

Sign in / Sign up

Export Citation Format

Share Document